From 94eb703c44538f3b3b76692afe48cc39c80495e7 Mon Sep 17 00:00:00 2001 From: Matthew Gregory Jones Date: Mon, 18 Jul 2022 13:40:41 -0700 Subject: [PATCH 01/37] inited ecdna birth death simulator.py --- cassiopeia/simulator/ecDNABirthDeathSimulator.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 cassiopeia/simulator/ecDNABirthDeathSimulator.py diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py new file mode 100644 index 00000000..e69de29b From d08ecb7ce9ffda9c774d16466aa1a10137d83c06 Mon Sep 17 00:00:00 2001 From: jpdsilva Date: Wed, 27 Jul 2022 14:02:55 -0700 Subject: [PATCH 02/37] updated BirthDeathFitnessSimulator for more flexibility --- .../simulator/BirthDeathFitnessSimulator.py | 212 +++++++---- notebooks/benchmark.ipynb | 329 +++--------------- 2 files changed, 201 insertions(+), 340 deletions(-) diff --git a/cassiopeia/simulator/BirthDeathFitnessSimulator.py b/cassiopeia/simulator/BirthDeathFitnessSimulator.py index 8ce79a51..e7c949df 100644 --- a/cassiopeia/simulator/BirthDeathFitnessSimulator.py +++ b/cassiopeia/simulator/BirthDeathFitnessSimulator.py @@ -146,7 +146,38 @@ def __init__( self.experiment_time = experiment_time self.collapse_unifurcations = collapse_unifurcations self.random_seed = random_seed + + def initialize_tree( + self, + names + ) -> nx.DiGraph: + ''' initializes a tree (nx.DiGraph() object with one node). Auxiliary data for each node is grabbed from self (initial conditions / params) or hardcoded + + Args: names is a generator (function object that stores internal state) that will be used to generate names for the tree nodes + Output: tree (DiGraph object with one node, the root) + root (name of root node in tree) + ''' + tree = nx.DiGraph() + root = next(names) + tree.add_node(root) + tree.nodes[root]["birth_scale"] = self.initial_birth_scale + tree.nodes[root]["time"] = 0 + + return tree, root + def make_initial_lineage_dict( + self, + id_value + ): + """ + uses self initial-conditions and hardcoded default parameters to create an initial lineage dict + Args: id_value: name of new lineage + + Output: a lineage dict + """ + lineage = self.make_lineage_dict(id_value, self.initial_birth_scale, 0, True) + return lineage + def simulate_tree( self, ) -> CassiopeiaTree: @@ -182,22 +213,25 @@ def node_name_generator() -> Generator[str, None, None]: if self.random_seed: np.random.seed(self.random_seed) - # Instantiate the implicit root - tree = nx.DiGraph() - root = next(names) - tree.add_node(root) - tree.nodes[root]["birth_scale"] = self.initial_birth_scale - tree.nodes[root]["time"] = 0 + tree, root = self.initialize_tree(names) + current_lineages = PriorityQueue() # Records the nodes that are observed at the end of the experiment + + # TO DO: update to accept arbitrary fields in the dict. observed_nodes = [] + + """ starting_lineage = { "id": root, "birth_scale": self.initial_birth_scale, "total_time": 0, "active": True, } + """ + starting_lineage = self.make_initial_lineage_dict(root) + # Sample the waiting time until the first division self.sample_lineage_event( starting_lineage, current_lineages, tree, names, observed_nodes @@ -219,6 +253,8 @@ def node_name_generator() -> Generator[str, None, None]: min_total_time = remaining_lineages[0]["total_time"] for lineage in remaining_lineages: parent = list(tree.predecessors(lineage["id"]))[0] + + # I don't think this is make_daughter_node; is this some kind of a node update? tree.nodes[lineage["id"]]["time"] += ( min_total_time - lineage["total_time"] ) @@ -321,6 +357,14 @@ def sample_lineage_event( # If birth and death would happen after the total experiment time, # just cut off the living branch length at the experiment time + """ + JP: what is the desired behaviour if + (lineage["total_time"] + birth_waiting_time + >= self.experiment_time) + and + (lineage["total_time"] + death_waiting_time + < self.experiment_time) ? + """ if ( self.experiment_time and lineage["total_time"] + birth_waiting_time @@ -328,22 +372,14 @@ def sample_lineage_event( and lineage["total_time"] + death_waiting_time >= self.experiment_time ): - tree.add_node(unique_id) - tree.nodes[unique_id]["birth_scale"] = lineage["birth_scale"] - tree.add_edge(lineage["id"], unique_id) - tree.nodes[unique_id]["time"] = self.experiment_time - - current_lineages.put( - ( - self.experiment_time, - unique_id, - { - "id": unique_id, - "birth_scale": lineage["birth_scale"], - "total_time": self.experiment_time, - "active": False, - }, - ) + + self.make_daughter(unique_id, + lineage["birth_scale"], + self.experiment_time, + False, # active = False + lineage, + current_lineages, + tree ) # Indicate this node is observed at the end of experiment @@ -351,54 +387,86 @@ def sample_lineage_event( else: if birth_waiting_time < death_waiting_time: + # Update birth rate updated_birth_scale = self.update_fitness( lineage["birth_scale"] ) # Annotate parameters for a given node in the tree - tree.add_node(unique_id) - tree.nodes[unique_id]["birth_scale"] = updated_birth_scale - tree.add_edge(lineage["id"], unique_id) - tree.nodes[unique_id]["time"] = ( - birth_waiting_time + lineage["total_time"] - ) - # Add the newly generated cell to the list of living lineages - current_lineages.put( - ( - birth_waiting_time + lineage["total_time"], - unique_id, - { - "id": unique_id, - "birth_scale": updated_birth_scale, - "total_time": birth_waiting_time - + lineage["total_time"], - "active": True, - }, - ) + self.make_daughter(unique_id, + updated_birth_scale, + birth_waiting_time + lineage["total_time"], + True, # active = True + lineage, + current_lineages, + tree ) else: - tree.add_node(unique_id) - tree.nodes[unique_id]["birth_scale"] = lineage["birth_scale"] - tree.add_edge(lineage["id"], unique_id) - tree.nodes[unique_id]["time"] = ( - death_waiting_time + lineage["total_time"] - ) - current_lineages.put( - ( - death_waiting_time + lineage["total_time"], - unique_id, - { - "id": unique_id, - "birth_scale": lineage["birth_scale"], - "total_time": death_waiting_time - + lineage["total_time"], - "active": False, - }, - ) + # replace with call to make_daughter_node + + self.make_daughter(unique_id, + lineage["birth_scale"], + death_waiting_time + lineage["total_time"], + False, + lineage, + current_lineages, + tree ) + # here, this could be a static method, but for ecDNA, we may want to access instance values to do the segregation, hence I'm writing this one an instance method (so that debugging will be the same) + def make_daughter( + self, + unique_id, + birth_scale, + updated_time, + active_flag, + lineage: Dict[str, Union[int, float]], + current_lineages: PriorityQueue, + tree: nx.DiGraph, + ): + """Updates the tree and current_lineages with a new daughter node / new lineage. + + Behavior: + 1. adds a new node unique_id to the tree with a directed edge from lineage["id"] to unique_id + 2. adds a new lineage with id unique_id to the current_lineages queue with appropriate data. + + Args: unique_id: id of new cell (lineage), will be used to name daughter node on tree and new lineage. + birth_scale: birth_scale parameter of new cell/lineage + updated_time: age of new node/lineage (JP: at time it divides?) + active_flag: bool to indicate whether lineage is active + lineage: the parent of the new lineage being created + + Modified args: + current_lineages: PriorityQueue of current lineages + tree: + + """ + # 1. compute any values to be updated by grabbing current values from the lineage Dict and performing operations on them as needed. (Grab my own CN array from the lineage) + + # 2. add daughter node to tree with appropriate parameters (my own CN array will go here into the new node) + tree.add_node(unique_id) + tree.nodes[unique_id]["birth_scale"] = birth_scale + tree.add_edge(lineage["id"], unique_id) + tree.nodes[unique_id]["time"] = ( + updated_time + ) + + # 3. put new lineage into current_lineages (CNs of my daughters will go here as cn_array_daughter_1 and cn_array_daughter_2) + current_lineages.put( + ( + updated_time, + unique_id, + self.make_lineage_dict( + unique_id, + birth_scale, + updated_time, + active_flag, + ), + ) + ) + def update_fitness(self, birth_scale: float) -> float: """Updates a lineage birth scale, which represents its fitness. @@ -435,3 +503,29 @@ def update_fitness(self, birth_scale: float) -> float: self.fitness_base ** self.fitness_distribution() ) return birth_scale * base_selection_coefficient + + # should this be a static method? + @staticmethod + def make_lineage_dict( + id_value, + birth_scale, + total_time, + active_flag, + ): + """makes a dict (lineage) from the given parameters. keys are hardcoded. + + Args: id_value: id of new lineage + birth_scale: birth_scale parameter of new lineage + total_time: age of lineage + active_flag: bool to indicate whether lineage is active + + Returns: a dict (lineage) with the parameter values under the hard-coded keys + + """ + lineage = { + "id": id_value, + "birth_scale": birth_scale, + "total_time": total_time, + "active": active_flag, + } + return lineage \ No newline at end of file diff --git a/notebooks/benchmark.ipynb b/notebooks/benchmark.ipynb index 924658de..d3b25b58 100755 --- a/notebooks/benchmark.ipynb +++ b/notebooks/benchmark.ipynb @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -76,29 +76,39 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can upload and export trees to iTOL which we can visualize in line. You can toggle branch lengths with the `use_branch_lengths` parameter. " + "We canNOT upload and export trees to iTOL which we can visualize in line. You can toggle branch lengths with the `use_branch_lengths` parameter. (iTOL is no longer free). Instead, we can use the local visualization library." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [ - "cas.pl.upload_and_export_itol(ground_truth_tree,\n", - " itol_config=\"~/.itolconfig_hidden\",\n", - " tree_name = \"simulated_tree\",\n", - " export_filepath=\"simulated_tree_example.png\",\n", - " use_branch_lengths=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "Image(\"simulated_tree_example.png\", width=500, height=500)" + "cas.pl.plot_matplotlib(ground_truth_tree, add_root=True)" ] }, { @@ -112,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -141,41 +151,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "We simulated 40 characters across 400 cells.\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "## inspect character matrix\n", "\n", @@ -211,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -227,35 +205,7 @@ "metadata": {}, "outputs": [], "source": [ - "# plot tree\n", - "\n", - "cas.pl.upload_and_export_itol(reconstructed_tree, itol_config=\"~/.itolconfig_hidden\", tree_name = \"reconstructed_simulated_tree\", export_filepath=\"reconstructed_simulated_tree.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": { - "image/png": { - "height": 500, - "width": 500 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image(\"reconstructed_simulated_tree.png\", width=500, height=500)" + "cas.pl.plot_matplotlib(reconstructed_tree, add_root=True)" ] }, { @@ -269,17 +219,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Normalized RF: 0.6981132075471698\n" - ] - } - ], + "outputs": [], "source": [ "reconstructed_tree.collapse_mutationless_edges(infer_ancestral_characters = True)\n", "\n", @@ -290,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -317,164 +259,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d30da3f741fc404aa8061bfe2a1cb2bf", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/10 [00:00" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sns.boxplot(data=algorithm_to_performance_triplets_df, x = 'Algorithm', y = 'Triplets')\n", "plt.show()\n", @@ -588,7 +350,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.13 ('ecass2')", "language": "python", "name": "python3" }, @@ -602,7 +364,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "7281e31e459af3472f32ee3e42bf55bc232e963b8420c818e7fe7d635207fd6b" + } } }, "nbformat": 4, From cc4a99e317e16ec0965ea0426db1efafcc7c7e37 Mon Sep 17 00:00:00 2001 From: jpdsilva Date: Fri, 12 Aug 2022 10:27:50 -0700 Subject: [PATCH 03/37] ecDNABirthDeathSimulator is broken / in process of adding binomial segregation to it. experimenting with BDFS --- .../simulator/ecDNABirthDeathSimulator.py | 279 ++++++++++++++++++ 1 file changed, 279 insertions(+) diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index e69de29b..df0b3b57 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -0,0 +1,279 @@ +""" +This file stores a general phylogenetic tree simulator using forward birth-death +process, including differing fitness on lineages on the tree. Allows for a +variety of division and fitness regimes to be specified by the user. +""" +from typing import Callable, Dict, Generator, List, Optional, Union + +import networkx as nx +import numpy as np +from queue import PriorityQueue + +from cassiopeia.data.CassiopeiaTree import CassiopeiaTree +from cassiopeia.mixins import TreeSimulatorError +# from cassiopeia.simulator.TreeSimulator import TreeSimulator +from cassiopeia.simulator.BirthDeathFitnessSimulator import BirthDeathFitnessSimulator + +class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): + """Simulator class for a forward birth-death process with fitness in a population with ecDNA. + + "Implements a flexible phylogenetic tree simulator using a forward birth- + death process. In this process starting from an initial root lineage, + births represent the branching of a new lineage and death represents the + cessation of an existing lineage. The process is represented as a tree, + with internal nodes representing division events, branch lengths + representing the lifetimes of individuals, and leaves representing samples + observed at the end of the experiment. + + Allows any distribution on birth and death waiting times to + be specified, including constant, exponential, weibull, etc. If no death + waiting time distribution is provided, the process reduces to a Yule birth + process. Also robustly simulates differing fitness on lineages within a + simulated tree. Fitness in this context represents potential mutations that + may be acquired on a lineage that change the rate at which new members are + born. Each lineage maintains its own birth scale parameter, altered from an + initial specified experiment-wide birth scale parameter by accrued + mutations. Different fitness regimes can be specified based on user + provided distributions on how often fitness mutations occur and their + respective strengths. + + There are two stopping conditions for the simulation. The first is "number + of extant nodes", which specifies the simulation to run until the first + moment a number of extant nodes exist. The second is "experiment time", + which specifies the time at which lineages are sampled. At least one of + these two stopping criteria must be provided. Both can be provided in which + case the simulation is run until one of the stopping conditions is reached." + + -(excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) + + Example use snippet: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) + # note that numpy uses a different parameterization of the + # exponential distribution with the scale parameter, which is 1/rate + + birth_waiting_distribution = lambda scale: np.random.exponential(scale) + death_waiting_distribution = np.random.exponential(1.5) + initial_birth_scale = 0.5 + mutation_distribution = lambda: 1 if np.random.uniform() > 0.5 else 0 + fitness_distribution = lambda: np.random.uniform(-1,1) + fitness_base = 2 + + bd_sim = BirthDeathFitnessSimulator( + birth_waiting_distribution, + initial_birth_scale, + death_waiting_distribution=death_waiting_distribution, + mutation_distribution=mutation_distribution, + fitness_distribution=fitness_distribution, + fitness_base=fitness_base, + num_extant=8 + ) + tree = bd_sim.simulate_tree() + + Args: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) + birth_waiting_distribution: A function that samples waiting times + from the birth distribution. Determines how often births occur. + Must take a scale parameter as the input + initial_birth_scale: The initial scale parameter that is used at the + start of the experiment + death_waiting_distribution: A function that samples waiting times + from the death distribution. Determines how often deaths occur + mutation_distribution: A function that samples the number of + mutations that occur at a division event. If None, then no + mutations are sampled + fitness_distribution: One of the two elements in determining the + multiplicative coefficient of a fitness mutation. A function that + samples the exponential that the fitness base is raised by. + Determines the distribution of fitness mutation strengths. Must not + be None if mutation_distribution provided + fitness_base: One of the two elements in determining the + multiplicative strength of a fitness mutation. The base that is + raised by the value given by the fitness distribution. Determines + the base strength of fitness mutations. By default is e, Euler's + Constant + num_extant: Specifies the number of extant lineages existing at the + same time as a stopping condition for the experiment + experiment_time: Specifies the total time that the experiment runs as a + stopping condition for the experiment + collapse_unifurcations: Specifies whether to collapse unifurcations in + the tree resulting from pruning dead lineages + random_seed: A seed for reproducibility + + Raises: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) + TreeSimulatorError if invalid stopping conditions are provided or if a + fitness distribution is not provided when a mutation distribution isn't + """ + + # update for ecDNA-specific initial conditions / simulation parameters. + def __init__( + self, + birth_waiting_distribution: Callable[[float], float], + initial_birth_scale: float, + death_waiting_distribution: Optional[ + Callable[[], float] + ] = lambda: np.inf, + mutation_distribution: Optional[Callable[[], int]] = None, + fitness_distribution: Optional[Callable[[], float]] = None, + fitness_base: float = np.e, + num_extant: Optional[int] = None, + experiment_time: Optional[float] = None, + collapse_unifurcations: bool = True, + random_seed: int = None, + initial_CN: np.array = np.array([1]), + ): + if num_extant is None and experiment_time is None: + raise TreeSimulatorError( + "Please specify at least one stopping condition" + ) + if mutation_distribution is not None and fitness_distribution is None: + raise TreeSimulatorError( + "Please specify a fitness strength distribution" + ) + if num_extant is not None and num_extant <= 0: + raise TreeSimulatorError( + "Please specify number of extant lineages greater than 0" + ) + if num_extant is not None and type(num_extant) is not int: + raise TreeSimulatorError( + "Please specify an integer number of extant tips" + ) + if experiment_time is not None and experiment_time <= 0: + raise TreeSimulatorError( + "Please specify an experiment time greater than 0" + ) + + self.birth_waiting_distribution = birth_waiting_distribution + self.initial_birth_scale = initial_birth_scale + self.death_waiting_distribution = death_waiting_distribution + self.mutation_distribution = mutation_distribution + self.fitness_distribution = fitness_distribution + self.fitness_base = fitness_base + self.num_extant = num_extant + self.experiment_time = experiment_time + self.collapse_unifurcations = collapse_unifurcations + self.random_seed = random_seed + self.initial_CN = initial_CN + + # update to store cn_array in node. (tree.nodes[root]["cn_array"]) + def initialize_tree( + self, + names + ) -> nx.DiGraph: + ''' initializes a tree (nx.DiGraph() object with one node) + ''' + tree = nx.DiGraph() + root = next(names) + tree.add_node(root) + tree.nodes[root]["birth_scale"] = self.initial_birth_scale + tree.nodes[root]["time"] = 0 + tree.nodes[root]["cn_array"] = self.initial_CN + + return tree, root + + # update to include self.initial_CN_array + def make_initial_lineage_dict( + self, + id_value + ): + lineage = self.make_lineage_dict(id_value, self.initial_birth_scale, 0, True, self.initial_CN) + return lineage + + + # update to compute fitness using lineage cn_array + def update_fitness(self, birth_scale: float) -> float: + """Updates a lineage birth scale, which represents its fitness. + + At each division event, the fitness is updated by sampling from a + distribution determining the number of mutations. The birth scale + parameter of the lineage is then scaled by the total multiplicative + coefficient across all mutations and passed on to the descendant + nodes. The multiplicative factor of each mutation is determined by + exponentiating a base parameter by a value drawn from another + 'fitness' distribution. Therefore, negative values from the fitness + distribution are valid and down-scale the birth scale parameter. + The base determines the base strength of the mutations in either + direction and the fitness distribution determines how the mutations + are distributed. + + Args: + birth_scale: The birth_scale to be updated + + Returns: + The updated birth_scale + + Raises: + TreeSimulatorError if a negative number of mutations is sampled + """ + base_selection_coefficient = 1 + if self.mutation_distribution: + num_mutations = int(self.mutation_distribution()) + if num_mutations < 0: + raise TreeSimulatorError( + "Negative number of mutations detected" + ) + for _ in range(num_mutations): + base_selection_coefficient *= ( + self.fitness_base ** self.fitness_distribution() + ) + return birth_scale * base_selection_coefficient + + # update to handle CN_array of self (in lineage and tree) and daughters (in lineage only) (see comments within function) + def make_daughter( + self, + unique_id, + birth_scale, + updated_time, + active_flag, + lineage: Dict[str, Union[int, float]], + current_lineages: PriorityQueue, + tree: nx.DiGraph, + daughter_ind: int + ): + """Updates the tree and current_lineages with a new daughter. + + Tree has a new node unique_id, with data + """ + # 1. compute any values to be updated by grabbing current values from the lineage Dict and performing operations on them as needed. + # (Grab my own CN array from the lineage) + + + # 2. add daughter node to tree with appropriate parameters (my own CN array will go here into the new node) + tree.add_node(unique_id) + tree.nodes[unique_id]["birth_scale"] = birth_scale + tree.add_edge(lineage["id"], unique_id) + tree.nodes[unique_id]["time"] = ( + updated_time + ) + + # 3. put new lineage into current_lineages (CNs of my daughters will go here as cn_array_daughter_1 and cn_array_daughter_2) + current_lineages.put( + ( + updated_time, + unique_id, + self.make_lineage_dict( + unique_id, + birth_scale, + updated_time, + active_flag, + ), + ) + ) + + + # should this be a static method? + # update to include ecDNA copy number + @staticmethod + def make_lineage_dict( + id_value, + birth_scale, + total_time, + active_flag, + cn_array, + ): + lineage = { + "id": id_value, + "birth_scale": birth_scale, + "total_time": total_time, + "active": active_flag, + "cn_array":cn_array, + } + return lineage + \ No newline at end of file From e1af41ece8cb91af365b9d229d96fcb1728ba8c2 Mon Sep 17 00:00:00 2001 From: jpdsilva Date: Fri, 12 Aug 2022 10:29:30 -0700 Subject: [PATCH 04/37] i don't think ecDNABirthDeathSimulator compiles, trying to add random seg. to it --- .../simulator/BirthDeathFitnessSimulator.py | 5 +- .../simulator/ecDNABirthDeathSimulator_v2.py | 429 ++++++++++++++++++ 2 files changed, 432 insertions(+), 2 deletions(-) create mode 100644 cassiopeia/simulator/ecDNABirthDeathSimulator_v2.py diff --git a/cassiopeia/simulator/BirthDeathFitnessSimulator.py b/cassiopeia/simulator/BirthDeathFitnessSimulator.py index e7c949df..9842725d 100644 --- a/cassiopeia/simulator/BirthDeathFitnessSimulator.py +++ b/cassiopeia/simulator/BirthDeathFitnessSimulator.py @@ -268,9 +268,9 @@ def node_name_generator() -> Generator[str, None, None]: # If the lineage is no longer active, just remove it from the queue. # This represents the time at which the lineage dies. if lineage["active"]: - for _ in range(2): + for i in range(2): self.sample_lineage_event( - lineage, current_lineages, tree, names, observed_nodes + lineage, current_lineages, tree, names, observed_nodes, i ) cassiopeia_tree = CassiopeiaTree(tree=tree) @@ -300,6 +300,7 @@ def sample_lineage_event( tree: nx.DiGraph, names: Generator, observed_nodes: List[str], + daughter_ind: int, ) -> None: """A helper function that samples an event for a lineage. diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator_v2.py b/cassiopeia/simulator/ecDNABirthDeathSimulator_v2.py new file mode 100644 index 00000000..99c19d4b --- /dev/null +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator_v2.py @@ -0,0 +1,429 @@ +""" +This file stores a general phylogenetic tree simulator using forward birth-death +process, including differing fitness on lineages on the tree. Allows for a +variety of division and fitness regimes to be specified by the user. +""" +from typing import Callable, Dict, Generator, List, Optional, Union + +import networkx as nx +import numpy as np +from queue import PriorityQueue + +from cassiopeia.data.CassiopeiaTree import CassiopeiaTree +from cassiopeia.mixins import TreeSimulatorError +from cassiopeia.simulator.TreeSimulator import TreeSimulator + + +class BirthDeathFitnessSimulator(TreeSimulator): + """Simulator class for a general forward birth-death process with fitness. + Implements a flexible phylogenetic tree simulator using a forward birth- + death process. In this process starting from an initial root lineage, + births represent the branching of a new lineage and death represents the + cessation of an existing lineage. The process is represented as a tree, + with internal nodes representing division events, branch lengths + representing the lifetimes of individuals, and leaves representing samples + observed at the end of the experiment. + Allows any distribution on birth and death waiting times to + be specified, including constant, exponential, weibull, etc. If no death + waiting time distribution is provided, the process reduces to a Yule birth + process. Also robustly simulates differing fitness on lineages within a + simulated tree. Fitness in this context represents potential mutations that + may be acquired on a lineage that change the rate at which new members are + born. Each lineage maintains its own birth scale parameter, altered from an + initial specified experiment-wide birth scale parameter by accrued + mutations. Different fitness regimes can be specified based on user + provided distributions on how often fitness mutations occur and their + respective strengths. + There are two stopping conditions for the simulation. The first is "number + of extant nodes", which specifies the simulation to run until the first + moment a number of extant nodes exist. The second is "experiment time", + which specifies the time at which lineages are sampled. At least one of + these two stopping criteria must be provided. Both can be provided in which + case the simulation is run until one of the stopping conditions is reached. + Example use snippet: + # note that numpy uses a different parameterization of the + # exponential distribution with the scale parameter, which is 1/rate + birth_waiting_distribution = lambda scale: np.random.exponential(scale) + death_waiting_distribution = np.random.exponential(1.5) + initial_birth_scale = 0.5 + mutation_distribution = lambda: 1 if np.random.uniform() > 0.5 else 0 + fitness_distribution = lambda: np.random.uniform(-1,1) + fitness_base = 2 + bd_sim = BirthDeathFitnessSimulator( + birth_waiting_distribution, + initial_birth_scale, + death_waiting_distribution=death_waiting_distribution, + mutation_distribution=mutation_distribution, + fitness_distribution=fitness_distribution, + fitness_base=fitness_base, + num_extant=8 + ) + tree = bd_sim.simulate_tree() + Args: + birth_waiting_distribution: A function that samples waiting times + from the birth distribution. Determines how often births occur. + Must take a scale parameter as the input + initial_birth_scale: The initial scale parameter that is used at the + start of the experiment + death_waiting_distribution: A function that samples waiting times + from the death distribution. Determines how often deaths occur + mutation_distribution: A function that samples the number of + mutations that occur at a division event. If None, then no + mutations are sampled + fitness_distribution: One of the two elements in determining the + multiplicative coefficient of a fitness mutation. A function that + samples the exponential that the fitness base is raised by. + Determines the distribution of fitness mutation strengths. Must not + be None if mutation_distribution provided + fitness_base: One of the two elements in determining the + multiplicative strength of a fitness mutation. The base that is + raised by the value given by the fitness distribution. Determines + the base strength of fitness mutations. By default is e, Euler's + Constant + num_extant: Specifies the number of extant lineages existing at the + same time as a stopping condition for the experiment + experiment_time: Specifies the total time that the experiment runs as a + stopping condition for the experiment + collapse_unifurcations: Specifies whether to collapse unifurcations in + the tree resulting from pruning dead lineages + random_seed: A seed for reproducibility + Raises: + TreeSimulatorError if invalid stopping conditions are provided or if a + fitness distribution is not provided when a mutation distribution isn't + """ + + def __init__( + self, + birth_waiting_distribution: Callable[[float], float], + initial_birth_scale: float, + death_waiting_distribution: Optional[ + Callable[[], float] + ] = lambda: np.inf, + mutation_distribution: Optional[Callable[[], int]] = None, + fitness_distribution: Optional[Callable[[], float]] = None, + fitness_base: float = np.e, + num_extant: Optional[int] = None, + experiment_time: Optional[float] = None, + collapse_unifurcations: bool = True, + random_seed: int = None, + ): + if num_extant is None and experiment_time is None: + raise TreeSimulatorError( + "Please specify at least one stopping condition" + ) + if mutation_distribution is not None and fitness_distribution is None: + raise TreeSimulatorError( + "Please specify a fitness strength distribution" + ) + if num_extant is not None and num_extant <= 0: + raise TreeSimulatorError( + "Please specify number of extant lineages greater than 0" + ) + if num_extant is not None and type(num_extant) is not int: + raise TreeSimulatorError( + "Please specify an integer number of extant tips" + ) + if experiment_time is not None and experiment_time <= 0: + raise TreeSimulatorError( + "Please specify an experiment time greater than 0" + ) + + self.birth_waiting_distribution = birth_waiting_distribution + self.initial_birth_scale = initial_birth_scale + self.death_waiting_distribution = death_waiting_distribution + self.mutation_distribution = mutation_distribution + self.fitness_distribution = fitness_distribution + self.fitness_base = fitness_base + self.num_extant = num_extant + self.experiment_time = experiment_time + self.collapse_unifurcations = collapse_unifurcations + self.random_seed = random_seed + + def simulate_tree( + self, + ) -> CassiopeiaTree: + """Simulates trees from a general birth/death process with fitness. + A forward-time birth/death process is simulated by tracking a series of + lineages and sampling event waiting times for each lineage. Each + lineage draws death waiting times from the same distribution, but + maintains its own birth scale parameter that determines the shape of + its birth waiting time distribution. At each division event, fitness + mutation events are sampled, and the birth scale parameter is scaled by + their multiplicative coefficients. This updated birth scale passed + onto successors. + Returns: + A CassiopeiaTree with the tree topology initialized with the + simulated tree + Raises: + TreeSimulatorError if all lineages die before a stopping condition + """ + + def node_name_generator() -> Generator[str, None, None]: + """Generates unique node names for the tree.""" + i = 0 + while True: + yield str(i) + i += 1 + + names = node_name_generator() + + # Set the seed + if self.random_seed: + np.random.seed(self.random_seed) + + # Instantiate the implicit root + tree = nx.DiGraph() + root = next(names) + tree.add_node(root) + tree.nodes[root]["birth_scale"] = self.initial_birth_scale + tree.nodes[root]["time"] = 0 + current_lineages = PriorityQueue() + # Records the nodes that are observed at the end of the experiment + observed_nodes = [] + starting_lineage = { + "id": root, + "birth_scale": self.initial_birth_scale, + "total_time": 0, + "active": True, + } + + # Sample the waiting time until the first division + self.sample_lineage_event( + starting_lineage, current_lineages, tree, names, observed_nodes + ) + + # Perform the process until there are no active extant lineages left + while not current_lineages.empty(): + # If number of extant lineages is the stopping criterion, at the + # first instance of having n extant tips, stop the experiment + # and set the total lineage time for each lineage to be equal to + # the minimum, to produce ultrametric trees. Also, the birth_scale + # parameter of each leaf is rolled back to equal its parent's. + if self.num_extant: + if current_lineages.qsize() == self.num_extant: + remaining_lineages = [] + while not current_lineages.empty(): + _, _, lineage = current_lineages.get() + remaining_lineages.append(lineage) + min_total_time = remaining_lineages[0]["total_time"] + for lineage in remaining_lineages: + parent = list(tree.predecessors(lineage["id"]))[0] + tree.nodes[lineage["id"]]["time"] += ( + min_total_time - lineage["total_time"] + ) + tree.nodes[lineage["id"]]["birth_scale"] = tree.nodes[ + parent + ]["birth_scale"] + observed_nodes.append(lineage["id"]) + break + # Pop the minimum age lineage to simulate forward time + _, _, lineage = current_lineages.get() + # If the lineage is no longer active, just remove it from the queue. + # This represents the time at which the lineage dies. + if lineage["active"]: + # grab parental copy number from lineage + + # perform binomial sampling on each element in the array + + # sample lineage event 1 + + # sample lineage event 2 + + for _ in range(2): + self.sample_lineage_event( + lineage, current_lineages, tree, names, observed_nodes + ) + + cassiopeia_tree = CassiopeiaTree(tree=tree) + time_dictionary = {} + for i in tree.nodes: + time_dictionary[i] = tree.nodes[i]["time"] + cassiopeia_tree.set_times(time_dictionary) + + # Prune dead lineages and collapse resulting unifurcations + to_remove = list(set(cassiopeia_tree.leaves) - set(observed_nodes)) + cassiopeia_tree.remove_leaves_and_prune_lineages(to_remove) + if self.collapse_unifurcations and len(cassiopeia_tree.nodes) > 1: + cassiopeia_tree.collapse_unifurcations(source="1") + + # If only implicit root remains after pruning dead lineages, error + if len(cassiopeia_tree.nodes) == 1: + raise TreeSimulatorError( + "All lineages died before stopping condition" + ) + + return cassiopeia_tree + + def sample_lineage_event( + self, + lineage: Dict[str, Union[int, float]], + current_lineages: PriorityQueue, + tree: nx.DiGraph, + names: Generator, + observed_nodes: List[str], + daughter_cn, + ) -> None: + """A helper function that samples an event for a lineage. + Takes a lineage and determines the next event in that lineage's + future. Simulates the lifespan of a new descendant. Birth and + death waiting times are sampled, representing how long the + descendant lived. If a death event occurs first, then the lineage + with the new descendant is added to the queue of currently alive, + but its status is marked as inactive and will be removed at the + time the lineage dies. If a birth event occurs first, then the + lineage with the new descendant is added to the queue, but with its + status marked as active, and further events will be sampled at the + time the lineage divides. Additionally, its fitness will be updated + by altering its birth rate. The descendant node is added to the + tree object, with the edge weight between the current node and the + descendant representing the lifespan of the descendant. In the + case the descendant would live past the end of the experiment (both + birth and death times exceed past the end of the experiment), then + the lifespan is cut off at the experiment time and a final observed + sample is added to the tree. In this case the lineage is marked as + inactive as well. + Args: + unique_id: The unique ID number to be used to name a new node + added to the tree + lineage: The current extant lineage to extend. Contains the ID + of the internal node to attach the descendant to, the + current birth scale parameter of the lineage, the current + total lived time of the lineage, and the status of whether + the lineage is still dividing + current_lineages: The queue containing currently alive lineages + tree: The tree object being constructed by the simulator + representing the birth death process + names: A generator providing unique names for tree nodes + observed_nodes: A list of nodes that are observed at the end of + the experiment + Raises: + TreeSimulatorError if a negative waiting time is sampled or a + non-active lineage is passed in + """ + if not lineage["active"]: + raise TreeSimulatorError( + "Cannot sample event for non-active lineage" + ) + + unique_id = next(names) + + # @JP: update to use daughter_CN too. + birth_waiting_time = self.birth_waiting_distribution( + lineage["birth_scale"] + ) + death_waiting_time = self.death_waiting_distribution() + if birth_waiting_time <= 0 or death_waiting_time <= 0: + raise TreeSimulatorError("0 or negative waiting time detected") + + # If birth and death would happen after the total experiment time, + # just cut off the living branch length at the experiment time + if ( + self.experiment_time + and lineage["total_time"] + birth_waiting_time + >= self.experiment_time + and lineage["total_time"] + death_waiting_time + >= self.experiment_time + ): + tree.add_node(unique_id) + tree.nodes[unique_id]["birth_scale"] = lineage["birth_scale"] + tree.add_edge(lineage["id"], unique_id) + tree.nodes[unique_id]["time"] = self.experiment_time + + current_lineages.put( + ( + self.experiment_time, + unique_id, + { + "id": unique_id, + "birth_scale": lineage["birth_scale"], + "total_time": self.experiment_time, + "active": False, + }, + ) + ) + + # Indicate this node is observed at the end of experiment + observed_nodes.append(unique_id) + + else: + if birth_waiting_time < death_waiting_time: + # Update birth rate + updated_birth_scale = self.update_fitness( + lineage["birth_scale"] + ) + + # Annotate parameters for a given node in the tree + tree.add_node(unique_id) + tree.nodes[unique_id]["birth_scale"] = updated_birth_scale + tree.add_edge(lineage["id"], unique_id) + tree.nodes[unique_id]["time"] = ( + birth_waiting_time + lineage["total_time"] + ) + # Add the newly generated cell to the list of living lineages + current_lineages.put( + ( + birth_waiting_time + lineage["total_time"], + unique_id, + { + "id": unique_id, + "birth_scale": updated_birth_scale, + "total_time": birth_waiting_time + + lineage["total_time"], + "active": True, + }, + ) + ) + + else: + tree.add_node(unique_id) + tree.nodes[unique_id]["birth_scale"] = lineage["birth_scale"] + tree.add_edge(lineage["id"], unique_id) + tree.nodes[unique_id]["time"] = ( + death_waiting_time + lineage["total_time"] + ) + current_lineages.put( + ( + death_waiting_time + lineage["total_time"], + unique_id, + { + "id": unique_id, + "birth_scale": lineage["birth_scale"], + "total_time": death_waiting_time + + lineage["total_time"], + "active": False, + }, + ) + ) + + def update_fitness(self, birth_scale: float) -> float: + """Updates a lineage birth scale, which represents its fitness. + At each division event, the fitness is updated by sampling from a + distribution determining the number of mutations. The birth scale + parameter of the lineage is then scaled by the total multiplicative + coefficient across all mutations and passed on to the descendant + nodes. The multiplicative factor of each mutation is determined by + exponentiating a base parameter by a value drawn from another + 'fitness' distribution. Therefore, negative values from the fitness + distribution are valid and down-scale the birth scale parameter. + The base determines the base strength of the mutations in either + direction and the fitness distribution determines how the mutations + are distributed. + Args: + birth_scale: The birth_scale to be updated + Returns: + The updated birth_scale + Raises: + TreeSimulatorError if a negative number of mutations is sampled + """ + base_selection_coefficient = 1 + if self.mutation_distribution: + num_mutations = int(self.mutation_distribution()) + if num_mutations < 0: + raise TreeSimulatorError( + "Negative number of mutations detected" + ) + for _ in range(num_mutations): + base_selection_coefficient *= ( + self.fitness_base ** self.fitness_distribution() + ) + return birth_scale * base_selection_coefficient \ No newline at end of file From 38989ee24c5b8152ec159f94da599afa3f989679 Mon Sep 17 00:00:00 2001 From: Matthew Gregory Jones Date: Fri, 12 Aug 2022 11:58:54 -0700 Subject: [PATCH 05/37] added ecdna simulator first pass, some tests started --- cassiopeia/mixins/errors.py | 4 + .../simulator/BirthDeathFitnessSimulator.py | 144 ++++----- cassiopeia/simulator/__init__.py | 2 + .../simulator/ecDNABirthDeathSimulator.py | 291 ++++++++++++------ .../birth_death_simulator_test.py | 4 + 5 files changed, 267 insertions(+), 178 deletions(-) diff --git a/cassiopeia/mixins/errors.py b/cassiopeia/mixins/errors.py index 1acd67fd..e3b9b8e2 100755 --- a/cassiopeia/mixins/errors.py +++ b/cassiopeia/mixins/errors.py @@ -27,6 +27,10 @@ class DistanceSolverError(Exception): pass +class ecDNABirthDeathSimulatorError(Exception): + """An ExceptionClass for ecDNABirthDeathSimulator class.""" + + pass class FitchCountError(Exception): """An ExceptionClass for FitchCount.""" diff --git a/cassiopeia/simulator/BirthDeathFitnessSimulator.py b/cassiopeia/simulator/BirthDeathFitnessSimulator.py index 9842725d..c04965cb 100644 --- a/cassiopeia/simulator/BirthDeathFitnessSimulator.py +++ b/cassiopeia/simulator/BirthDeathFitnessSimulator.py @@ -270,7 +270,7 @@ def node_name_generator() -> Generator[str, None, None]: if lineage["active"]: for i in range(2): self.sample_lineage_event( - lineage, current_lineages, tree, names, observed_nodes, i + lineage, current_lineages, tree, names, observed_nodes ) cassiopeia_tree = CassiopeiaTree(tree=tree) @@ -300,10 +300,8 @@ def sample_lineage_event( tree: nx.DiGraph, names: Generator, observed_nodes: List[str], - daughter_ind: int, ) -> None: """A helper function that samples an event for a lineage. - Takes a lineage and determines the next event in that lineage's future. Simulates the lifespan of a new descendant. Birth and death waiting times are sampled, representing how long the @@ -322,7 +320,6 @@ def sample_lineage_event( the lifespan is cut off at the experiment time and a final observed sample is added to the tree. In this case the lineage is marked as inactive as well. - Args: unique_id: The unique ID number to be used to name a new node added to the tree @@ -337,7 +334,6 @@ def sample_lineage_event( names: A generator providing unique names for tree nodes observed_nodes: A list of nodes that are observed at the end of the experiment - Raises: TreeSimulatorError if a negative waiting time is sampled or a non-active lineage is passed in @@ -358,14 +354,6 @@ def sample_lineage_event( # If birth and death would happen after the total experiment time, # just cut off the living branch length at the experiment time - """ - JP: what is the desired behaviour if - (lineage["total_time"] + birth_waiting_time - >= self.experiment_time) - and - (lineage["total_time"] + death_waiting_time - < self.experiment_time) ? - """ if ( self.experiment_time and lineage["total_time"] + birth_waiting_time @@ -373,14 +361,22 @@ def sample_lineage_event( and lineage["total_time"] + death_waiting_time >= self.experiment_time ): - - self.make_daughter(unique_id, - lineage["birth_scale"], - self.experiment_time, - False, # active = False - lineage, - current_lineages, - tree + tree.add_node(unique_id) + tree.nodes[unique_id]["birth_scale"] = lineage["birth_scale"] + tree.add_edge(lineage["id"], unique_id) + tree.nodes[unique_id]["time"] = self.experiment_time + + current_lineages.put( + ( + self.experiment_time, + unique_id, + { + "id": unique_id, + "birth_scale": lineage["birth_scale"], + "total_time": self.experiment_time, + "active": False, + }, + ) ) # Indicate this node is observed at the end of experiment @@ -388,85 +384,53 @@ def sample_lineage_event( else: if birth_waiting_time < death_waiting_time: - # Update birth rate updated_birth_scale = self.update_fitness( lineage["birth_scale"] ) # Annotate parameters for a given node in the tree - self.make_daughter(unique_id, - updated_birth_scale, - birth_waiting_time + lineage["total_time"], - True, # active = True - lineage, - current_lineages, - tree + tree.add_node(unique_id) + tree.nodes[unique_id]["birth_scale"] = updated_birth_scale + tree.add_edge(lineage["id"], unique_id) + tree.nodes[unique_id]["time"] = ( + birth_waiting_time + lineage["total_time"] + ) + # Add the newly generated cell to the list of living lineages + current_lineages.put( + ( + birth_waiting_time + lineage["total_time"], + unique_id, + { + "id": unique_id, + "birth_scale": updated_birth_scale, + "total_time": birth_waiting_time + + lineage["total_time"], + "active": True, + }, + ) ) else: - # replace with call to make_daughter_node - - self.make_daughter(unique_id, - lineage["birth_scale"], - death_waiting_time + lineage["total_time"], - False, - lineage, - current_lineages, - tree + tree.add_node(unique_id) + tree.nodes[unique_id]["birth_scale"] = lineage["birth_scale"] + tree.add_edge(lineage["id"], unique_id) + tree.nodes[unique_id]["time"] = ( + death_waiting_time + lineage["total_time"] + ) + current_lineages.put( + ( + death_waiting_time + lineage["total_time"], + unique_id, + { + "id": unique_id, + "birth_scale": lineage["birth_scale"], + "total_time": death_waiting_time + + lineage["total_time"], + "active": False, + }, + ) ) - - # here, this could be a static method, but for ecDNA, we may want to access instance values to do the segregation, hence I'm writing this one an instance method (so that debugging will be the same) - def make_daughter( - self, - unique_id, - birth_scale, - updated_time, - active_flag, - lineage: Dict[str, Union[int, float]], - current_lineages: PriorityQueue, - tree: nx.DiGraph, - ): - """Updates the tree and current_lineages with a new daughter node / new lineage. - - Behavior: - 1. adds a new node unique_id to the tree with a directed edge from lineage["id"] to unique_id - 2. adds a new lineage with id unique_id to the current_lineages queue with appropriate data. - - Args: unique_id: id of new cell (lineage), will be used to name daughter node on tree and new lineage. - birth_scale: birth_scale parameter of new cell/lineage - updated_time: age of new node/lineage (JP: at time it divides?) - active_flag: bool to indicate whether lineage is active - lineage: the parent of the new lineage being created - - Modified args: - current_lineages: PriorityQueue of current lineages - tree: - - """ - # 1. compute any values to be updated by grabbing current values from the lineage Dict and performing operations on them as needed. (Grab my own CN array from the lineage) - - # 2. add daughter node to tree with appropriate parameters (my own CN array will go here into the new node) - tree.add_node(unique_id) - tree.nodes[unique_id]["birth_scale"] = birth_scale - tree.add_edge(lineage["id"], unique_id) - tree.nodes[unique_id]["time"] = ( - updated_time - ) - - # 3. put new lineage into current_lineages (CNs of my daughters will go here as cn_array_daughter_1 and cn_array_daughter_2) - current_lineages.put( - ( - updated_time, - unique_id, - self.make_lineage_dict( - unique_id, - birth_scale, - updated_time, - active_flag, - ), - ) - ) def update_fitness(self, birth_scale: float) -> float: """Updates a lineage birth scale, which represents its fitness. diff --git a/cassiopeia/simulator/__init__.py b/cassiopeia/simulator/__init__.py index a7118473..1b510e75 100755 --- a/cassiopeia/simulator/__init__.py +++ b/cassiopeia/simulator/__init__.py @@ -5,6 +5,7 @@ from .Cas9LineageTracingDataSimulator import Cas9LineageTracingDataSimulator from .CompleteBinarySimulator import CompleteBinarySimulator from .DataSimulator import DataSimulator +from .ecDNABirthDeathSimulator import ecDNABirthDeathSimulator from .LeafSubsampler import LeafSubsampler from .LineageTracingDataSimulator import LineageTracingDataSimulator from .SimpleFitSubcloneSimulator import SimpleFitSubcloneSimulator @@ -19,6 +20,7 @@ "Cas9LineageTracingDataSimulator", "CompleteBinarySimulator", "DataSimulator", + "ecDNABirthDeathSimulator", "LeafSubsampler", "LineageTracingDataSimulator", "SimpleFitSubcloneSimulator", diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index df0b3b57..513db536 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -9,14 +9,15 @@ import numpy as np from queue import PriorityQueue -from cassiopeia.data.CassiopeiaTree import CassiopeiaTree -from cassiopeia.mixins import TreeSimulatorError -# from cassiopeia.simulator.TreeSimulator import TreeSimulator -from cassiopeia.simulator.BirthDeathFitnessSimulator import BirthDeathFitnessSimulator +from cassiopeia.mixins import ecDNABirthDeathSimulatorError +from cassiopeia.simulator.BirthDeathFitnessSimulator import ( + BirthDeathFitnessSimulator, +) + class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): - """Simulator class for a forward birth-death process with fitness in a population with ecDNA. - + """Simulator class for a forward birth-death process with fitness in a population with ecDNA. + "Implements a flexible phylogenetic tree simulator using a forward birth- death process. In this process starting from an initial root lineage, births represent the branching of a new lineage and death represents the @@ -44,9 +45,9 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): these two stopping criteria must be provided. Both can be provided in which case the simulation is run until one of the stopping conditions is reached." - -(excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) + -(excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) - Example use snippet: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) + Example use snippet: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) # note that numpy uses a different parameterization of the # exponential distribution with the scale parameter, which is 1/rate @@ -68,7 +69,7 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): ) tree = bd_sim.simulate_tree() - Args: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) + Args: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) birth_waiting_distribution: A function that samples waiting times from the birth distribution. Determines how often births occur. Must take a scale parameter as the input @@ -96,13 +97,14 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): collapse_unifurcations: Specifies whether to collapse unifurcations in the tree resulting from pruning dead lineages random_seed: A seed for reproducibility + initial_copy_number: Initial copy number for parental lineage. - Raises: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) + Raises: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) TreeSimulatorError if invalid stopping conditions are provided or if a fitness distribution is not provided when a mutation distribution isn't """ - # update for ecDNA-specific initial conditions / simulation parameters. + # update for ecDNA-specific initial conditions / simulation parameters. def __init__( self, birth_waiting_distribution: Callable[[float], float], @@ -117,7 +119,10 @@ def __init__( experiment_time: Optional[float] = None, collapse_unifurcations: bool = True, random_seed: int = None, - initial_CN: np.array = np.array([1]), + initial_copy_number: np.array = np.array([1]), + splitting_function: Callable[int, int] = lambda x: np.random.binomial( + x, p=0.5 + ), ): if num_extant is None and experiment_time is None: raise TreeSimulatorError( @@ -150,34 +155,22 @@ def __init__( self.experiment_time = experiment_time self.collapse_unifurcations = collapse_unifurcations self.random_seed = random_seed - self.initial_CN = initial_CN + self.initial_copy_number = initial_copy_number + self.splitting_function = splitting_function # update to store cn_array in node. (tree.nodes[root]["cn_array"]) - def initialize_tree( - self, - names - ) -> nx.DiGraph: - ''' initializes a tree (nx.DiGraph() object with one node) - ''' - tree = nx.DiGraph() - root = next(names) - tree.add_node(root) - tree.nodes[root]["birth_scale"] = self.initial_birth_scale - tree.nodes[root]["time"] = 0 - tree.nodes[root]["cn_array"] = self.initial_CN - - return tree, root - - # update to include self.initial_CN_array - def make_initial_lineage_dict( - self, - id_value - ): - lineage = self.make_lineage_dict(id_value, self.initial_birth_scale, 0, True, self.initial_CN) - return lineage - + def initialize_tree(self, names) -> nx.DiGraph: + """initializes a tree (nx.DiGraph() object with one node)""" + tree = nx.DiGraph() + root = next(names) + tree.add_node(root) + tree.nodes[root]["birth_scale"] = self.initial_birth_scale + tree.nodes[root]["time"] = 0 + tree.nodes[root]["ecdna_array"] = self.initial_copy_number + + return tree, root - # update to compute fitness using lineage cn_array + # update to compute fitness using lineage cn_array def update_fitness(self, birth_scale: float) -> float: """Updates a lineage birth scale, which represents its fitness. @@ -206,7 +199,7 @@ def update_fitness(self, birth_scale: float) -> float: if self.mutation_distribution: num_mutations = int(self.mutation_distribution()) if num_mutations < 0: - raise TreeSimulatorError( + raise ecDNABirthDeathSimulatorError( "Negative number of mutations detected" ) for _ in range(num_mutations): @@ -215,65 +208,187 @@ def update_fitness(self, birth_scale: float) -> float: ) return birth_scale * base_selection_coefficient - # update to handle CN_array of self (in lineage and tree) and daughters (in lineage only) (see comments within function) - def make_daughter( + def sample_lineage_event( self, - unique_id, - birth_scale, - updated_time, - active_flag, lineage: Dict[str, Union[int, float]], current_lineages: PriorityQueue, tree: nx.DiGraph, - daughter_ind: int - ): - """Updates the tree and current_lineages with a new daughter. - - Tree has a new node unique_id, with data + names: Generator, + observed_nodes: List[str], + ) -> None: + """A helper function that samples an event for a lineage. + Takes a lineage and determines the next event in that lineage's + future. Simulates the lifespan of a new descendant. Birth and + death waiting times are sampled, representing how long the + descendant lived. If a death event occurs first, then the lineage + with the new descendant is added to the queue of currently alive, + but its status is marked as inactive and will be removed at the + time the lineage dies. If a birth event occurs first, then the + lineage with the new descendant is added to the queue, but with its + status marked as active, and further events will be sampled at the + time the lineage divides. Additionally, its fitness will be updated + by altering its birth rate. The descendant node is added to the + tree object, with the edge weight between the current node and the + descendant representing the lifespan of the descendant. In the + case the descendant would live past the end of the experiment (both + birth and death times exceed past the end of the experiment), then + the lifespan is cut off at the experiment time and a final observed + sample is added to the tree. In this case the lineage is marked as + inactive as well. + Args: + unique_id: The unique ID number to be used to name a new node + added to the tree + lineage: The current extant lineage to extend. Contains the ID + of the internal node to attach the descendant to, the + current birth scale parameter of the lineage, the current + total lived time of the lineage, and the status of whether + the lineage is still dividing + current_lineages: The queue containing currently alive lineages + tree: The tree object being constructed by the simulator + representing the birth death process + names: A generator providing unique names for tree nodes + observed_nodes: A list of nodes that are observed at the end of + the experiment + Raises: + TreeSimulatorError if a negative waiting time is sampled or a + non-active lineage is passed in """ - # 1. compute any values to be updated by grabbing current values from the lineage Dict and performing operations on them as needed. - # (Grab my own CN array from the lineage) + if not lineage["active"]: + raise TreeSimulatorError( + "Cannot sample event for non-active lineage" + ) + unique_id = next(names) - # 2. add daughter node to tree with appropriate parameters (my own CN array will go here into the new node) - tree.add_node(unique_id) - tree.nodes[unique_id]["birth_scale"] = birth_scale - tree.add_edge(lineage["id"], unique_id) - tree.nodes[unique_id]["time"] = ( - updated_time + birth_waiting_time = self.birth_waiting_distribution( + lineage["birth_scale"] ) + death_waiting_time = self.death_waiting_distribution() + if birth_waiting_time <= 0 or death_waiting_time <= 0: + raise TreeSimulatorError("0 or negative waiting time detected") + + # If birth and death would happen after the total experiment time, + # just cut off the living branch length at the experiment time + if ( + self.experiment_time + and lineage["total_time"] + birth_waiting_time + >= self.experiment_time + and lineage["total_time"] + death_waiting_time + >= self.experiment_time + ): + + tree.add_node(unique_id) + tree.nodes[unique_id]["birth_scale"] = lineage["birth_scale"] + tree.add_edge(lineage["id"], unique_id) + tree.nodes[unique_id]["time"] = self.experiment_time + tree.nodes[unique_id]["ecdna_array"] = tree.nodes[lineage["id"]][ + "ecdna_array" + ] - # 3. put new lineage into current_lineages (CNs of my daughters will go here as cn_array_daughter_1 and cn_array_daughter_2) - current_lineages.put( - ( - updated_time, - unique_id, - self.make_lineage_dict( - unique_id, - birth_scale, - updated_time, - active_flag, - ), + current_lineages.put( + ( + self.experiment_time, + unique_id, + { + "id": unique_id, + "birth_scale": lineage["birth_scale"], + "total_time": self.experiment_time, + "active": False, + }, + ) ) - ) + # Indicate this node is observed at the end of experiment + observed_nodes.append(unique_id) - # should this be a static method? - # update to include ecDNA copy number - @staticmethod - def make_lineage_dict( - id_value, - birth_scale, - total_time, - active_flag, - cn_array, - ): - lineage = { - "id": id_value, - "birth_scale": birth_scale, - "total_time": total_time, - "active": active_flag, - "cn_array":cn_array, - } - return lineage - \ No newline at end of file + else: + if birth_waiting_time < death_waiting_time: + # Update birth rate + updated_birth_scale = self.update_fitness( + lineage["birth_scale"] + ) + + child_ecdna_array = self.get_ecdna_array(lineage["id"]) + + # Annotate parameters for a given node in the tree + tree.add_node(unique_id) + tree.nodes[unique_id]["birth_scale"] = updated_birth_scale + tree.add_edge(lineage["id"], unique_id) + tree.nodes[unique_id]["time"] = ( + birth_waiting_time + lineage["total_time"] + ) + tree.nodes[unique_id]["ecdna_array"] = child_ecdna_array + # Add the newly generated cell to the list of living lineages + current_lineages.put( + ( + birth_waiting_time + lineage["total_time"], + unique_id, + { + "id": unique_id, + "birth_scale": updated_birth_scale, + "total_time": birth_waiting_time + + lineage["total_time"], + "active": True, + }, + ) + ) + + else: + tree.add_node(unique_id) + tree.nodes[unique_id]["birth_scale"] = lineage["birth_scale"] + tree.add_edge(lineage["id"], unique_id) + tree.nodes[unique_id]["time"] = ( + death_waiting_time + lineage["total_time"] + ) + tree.nodes[unique_id]["ecdna_array"] = tree.nodes[ + lineage["id"] + ]["ecdna_array"] + current_lineages.put( + ( + death_waiting_time + lineage["total_time"], + unique_id, + { + "id": unique_id, + "birth_scale": lineage["birth_scale"], + "total_time": death_waiting_time + + lineage["total_time"], + "active": False, + }, + ) + ) + + def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: + """Generates an ecDNA array for a child given its parent and sister information. + + Args: + parent_id: ID of parent in the generated tree. + tree: The in-progress tree. + + Returns: + Numpy array corresponding to the ecDNA copy numbers for the new child. + """ + + parental_ecdna_array = 2*tree.nodes[parent_id]["ecdna_array"] + + has_child = tree.out_degree(parent_id) > 0 + + new_ecdna_array = parental_ecdna_array.copy() + + if has_child: + child_id = [n for n in tree.successors(parent_id)][0] + child_ecdna_array = tree.nodes[child_id]["ecdna_array"] + + new_ecdna_array = parental_ecdna_array - child_ecdna_array + else: + + new_ecdna_array = [ + self.splitting_function(n) for n in parental_ecdna_array + ] + + # check that new ecdnay array entries do not exceed parental entries + if np.any(new_ecdna_array > parental_ecdna_array): + raise ecDNABirthDeathSimulatorError( + "Child ecDNA entries exceed parental entries." + ) + + return new_ecdna_array diff --git a/test/simulator_tests/birth_death_simulator_test.py b/test/simulator_tests/birth_death_simulator_test.py index f7197a79..7bdd22f6 100644 --- a/test/simulator_tests/birth_death_simulator_test.py +++ b/test/simulator_tests/birth_death_simulator_test.py @@ -1,3 +1,7 @@ +""" +Tests the functionality of cassiopeia.simulator.BirthDeathFitnessSimulator. +""" + import unittest import networkx as nx From 3c2cc6bc75e5a686ef63a6277bb58d5ea1b85901 Mon Sep 17 00:00:00 2001 From: Matthew Gregory Jones Date: Fri, 12 Aug 2022 11:59:01 -0700 Subject: [PATCH 06/37] added ecdna simulator first pass, some tests started --- .../ecdna_birth_death_simulator_test.py | 82 +++++++++++++++++++ 1 file changed, 82 insertions(+) create mode 100644 test/simulator_tests/ecdna_birth_death_simulator_test.py diff --git a/test/simulator_tests/ecdna_birth_death_simulator_test.py b/test/simulator_tests/ecdna_birth_death_simulator_test.py new file mode 100644 index 00000000..d43d6b5d --- /dev/null +++ b/test/simulator_tests/ecdna_birth_death_simulator_test.py @@ -0,0 +1,82 @@ +""" +Tests the functionality of cassiopeia.simulator.ecDNABirthDeathSimulator. +""" + +from tkinter.tix import Tree +import unittest + +import networkx as nx +import numpy as np + +from typing import List, Tuple + +from cassiopeia.data.CassiopeiaTree import CassiopeiaTree +from cassiopeia.mixins import ecDNABirthDeathSimulatorError +from cassiopeia.simulator.ecDNABirthDeathSimulator import ( + ecDNABirthDeathSimulator, +) + +import cassiopeia.data.utilities as utilities + + +def extract_tree_statistics( + tree: CassiopeiaTree, +) -> Tuple[List[float], int, bool]: + """A helper function for testing simulated trees. + + Outputs the total lived time for each extant lineage, the number of extant + lineages, and whether the tree has the expected node degrees (to ensure + unifurcations were collapsed). + + Args: + tree: The tree to test + + Returns: + The total time lived for each leaf, the number of leaves, and if the + degrees only have degree 0 or 2 + """ + + times = [] + out_degrees = [] + for i in tree.nodes: + if tree.is_leaf(i): + times.append(tree.get_time(i)) + out_degrees.append(len(tree.children(i))) + out_degrees.pop(0) + + correct_degrees = all(x == 2 or x == 0 for x in out_degrees) + + return times, len(times), correct_degrees + + +class ecDNABirthDeathSimulatorTest(unittest.TestCase): + + def test_ecdna_splitting(self): + + np.random.seed(41) + sim = ecDNABirthDeathSimulator(lambda _: 1, 1, num_extant=16) + + tree = nx.DiGraph() + tree.add_node(0) + + tree.nodes[0]['ecdna_array'] = np.array([4, 5]) + + # test new generation of child + new_ecdna_array = sim.get_ecdna_array(0, tree) + expected_array = [0, 0] # manually find out and set + self.assertTrue(np.all(expected_array == new_ecdna_array)) + + # test generation when child already exists + tree.add_edge(0, 1) + tree.nodes[1]['ecdna_array'] = np.array([5, 7]) + new_ecdna_array = sim.get_ecdna_array(0, tree) + expected_array = [3, 3] # manually find out and set + self.assertTrue(np.all(expected_array == new_ecdna_array)) + + + + + + +if __name__ == "__main__": + unittest.main() From 6feeeb591b757a85fb7146d598c26cb69c49dab2 Mon Sep 17 00:00:00 2001 From: jpdsilva Date: Sun, 21 Aug 2022 19:06:55 -0700 Subject: [PATCH 07/37] debugging ecDNABirthDeathSimulator.py --- .../simulator/ecDNABirthDeathSimulator.py | 44 +- notebooks/ecDNA_sim_test.ipynb | 517 ++++++++++++++++++ 2 files changed, 557 insertions(+), 4 deletions(-) create mode 100644 notebooks/ecDNA_sim_test.ipynb diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 513db536..03a1779c 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -98,7 +98,8 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): the tree resulting from pruning dead lineages random_seed: A seed for reproducibility initial_copy_number: Initial copy number for parental lineage. - + splitting_function: As implemented, the function that describes segregation of each species at cell division. TO DO: fix this implementation to allow for non-independent segregation. + Raises: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) TreeSimulatorError if invalid stopping conditions are provided or if a fitness distribution is not provided when a mutation distribution isn't @@ -267,6 +268,41 @@ def sample_lineage_event( if birth_waiting_time <= 0 or death_waiting_time <= 0: raise TreeSimulatorError("0 or negative waiting time detected") + # TO DO: this is a really hacky fix b/c it bypasses the length checks of whether the first birth_waiting_time exceeds self.experiment_time. Also, it just assumes the first event is a birth. we could also WOLOG that the first birth_waiting_time of the experiment is 0 (but that requires shifting times elsewhere in order to permit correct model comparison to non-ecDNA simulators. + if lineage["total_time"] == 0: + print("current_lineages is empty") + print("\n") + # Update birth rate + updated_birth_scale = self.update_fitness( + lineage["birth_scale"] + ) + + #child_ecdna_array = self.get_ecdna_array(lineage["id"],tree) + + # Annotate parameters for a given node in the tree + tree.add_node(unique_id) + tree.nodes[unique_id]["birth_scale"] = updated_birth_scale + tree.add_edge(lineage["id"], unique_id) + tree.nodes[unique_id]["time"] = ( + birth_waiting_time + lineage["total_time"] + ) + tree.nodes[unique_id]["ecdna_array"] = tree.nodes[lineage["id"]]["ecdna_array"] # child_ecdna_array + # Add the newly generated cell to the list of living lineages + current_lineages.put( + ( + birth_waiting_time + lineage["total_time"], + unique_id, + { + "id": unique_id, + "birth_scale": updated_birth_scale, + "total_time": birth_waiting_time + + lineage["total_time"], + "active": True, + }, + ) + ) + return + # If birth and death would happen after the total experiment time, # just cut off the living branch length at the experiment time if ( @@ -308,7 +344,7 @@ def sample_lineage_event( lineage["birth_scale"] ) - child_ecdna_array = self.get_ecdna_array(lineage["id"]) + child_ecdna_array = self.get_ecdna_array(lineage["id"],tree) # Annotate parameters for a given node in the tree tree.add_node(unique_id) @@ -381,9 +417,9 @@ def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: new_ecdna_array = parental_ecdna_array - child_ecdna_array else: - new_ecdna_array = [ + new_ecdna_array = np.array([ self.splitting_function(n) for n in parental_ecdna_array - ] + ]) # check that new ecdnay array entries do not exceed parental entries if np.any(new_ecdna_array > parental_ecdna_array): diff --git a/notebooks/ecDNA_sim_test.ipynb b/notebooks/ecDNA_sim_test.ipynb new file mode 100644 index 00000000..21c481d3 --- /dev/null +++ b/notebooks/ecDNA_sim_test.ipynb @@ -0,0 +1,517 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cassiopeia Benchmarking Pipeline\n", + "\n", + "Here, we simulate trees with ecDNA. Based on benchmark.ipynb \n", + "\n", + "\"You can install Cassiopeia by following the guide [here](https://cassiopeia-lineage.readthedocs.io/en/latest/installation.html).\"\n", + "\n", + "\"All of our documentation is hosted [here](https://cassiopeia-lineage.readthedocs.io/en/latest/).\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import cProfile\n", + "from collections import defaultdict\n", + "import copy\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import time\n", + "from tqdm.auto import tqdm\n", + "\n", + "import cassiopeia as cas\n", + "from cassiopeia.solver import missing_data_methods" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.inf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tree Simulation\n", + "\n", + "ecDNABirthDeathSimulator is based on BirthDeathFitnesSimulator, which is documented [here](https://cassiopeia-lineage.readthedocs.io/en/latest/api/reference/cassiopeia.sim.BirthDeathFitnessSimulator.html). " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current_lineages is empty\n", + "\n", + "\n" + ] + } + ], + "source": [ + "bd_sim = cas.sim.ecDNABirthDeathSimulator(\n", + " birth_waiting_distribution = lambda scale: np.random.exponential(scale),\n", + " initial_birth_scale = 0.5,\n", + " death_waiting_distribution = lambda: np.inf, # lambda: np.random.exponential(1.5),\n", + " mutation_distribution = lambda: 1 if np.random.uniform() < 0.5 else 0,\n", + " fitness_distribution = lambda: np.random.normal(0, .5),\n", + " fitness_base = 1.3,\n", + " num_extant = 4,\n", + " random_seed=17, \n", + " initial_copy_number = np.array([7,3,5]),\n", + " splitting_function = lambda x: np.random.binomial(x, p=0.5),\n", + ")\n", + "ground_truth_tree = bd_sim.simulate_tree()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's stare manually at a few ecDNA CNs. One weird thing I'm noticing is that there isn't conservation of copy number right now. This could be b/c of death. so I'll re-run with infinite time to death.\n", + "I tried this and it still doesn't conserve CN, which is weird. \n", + "\n", + "I figured out why it doesn't conserve CN. It's because there's a weird passage of cell 0 to cell 1 before division is allowed to start occurring. Let's figure out where/why this happens and stop it. \n", + "\n", + "It happens in simulate_tree (inherited from BirthDeathSimulator), because in order to sample the first waiting time, we call sample_lineage_event. I fixed this in a really hack-y way (see ecDNABirthDeathSimulator.py, search for \"TO DO\" to see what I mean). No wait, this fix doesn't work; it seems liek the bypass is *always* being triggered. Let's stare at that and try to see why.\n", + "\n", + "I didn't figure out why the other condition in sample_lineage_event didn't work, but this one (checking whether we're at time 0) does. So i'll go with it for now.\n", + "\n", + "I manually checked that the ecDNA CN are doubling every generation (for two generations).\n", + "\n", + "Where is the rng getting seeded? B/c it seems like it is, and in a setting where the long-time dynamics of the simulations depend heavily on the first division, it's quite important to be aware of seeding!" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'graph': {},\n", + " '_node': {'0': {'birth_scale': 0.5,\n", + " 'time': 0,\n", + " 'ecdna_array': array([7, 3, 5]),\n", + " 'character_states': []},\n", + " '1': {'birth_scale': 0.5,\n", + " 'time': 0.17454120776779053,\n", + " 'ecdna_array': array([7, 3, 5]),\n", + " 'character_states': []},\n", + " '2': {'birth_scale': 0.5426436139245218,\n", + " 'time': 0.28084136317063757,\n", + " 'ecdna_array': array([8, 3, 2]),\n", + " 'character_states': []},\n", + " '3': {'birth_scale': 0.5,\n", + " 'time': 0.3959795486358011,\n", + " 'ecdna_array': array([6, 3, 8]),\n", + " 'character_states': []},\n", + " '4': {'birth_scale': 0.5426436139245218,\n", + " 'time': 0.3144434495424206,\n", + " 'ecdna_array': array([10, 1, 2]),\n", + " 'character_states': []},\n", + " '5': {'birth_scale': 0.5426436139245218,\n", + " 'time': 0.3959795486358011,\n", + " 'ecdna_array': array([6, 5, 2]),\n", + " 'character_states': []},\n", + " '6': {'birth_scale': 0.5426436139245218,\n", + " 'time': 0.3959795486358011,\n", + " 'ecdna_array': array([9, 1, 2]),\n", + " 'character_states': []},\n", + " '7': {'birth_scale': 0.5426436139245218,\n", + " 'time': 0.3959795486358011,\n", + " 'ecdna_array': array([11, 1, 2]),\n", + " 'character_states': []}},\n", + " '_adj': {'0': {'1': {'length': 0.17454120776779053}},\n", + " '1': {'2': {'length': 0.10630015540284704},\n", + " '3': {'length': 0.22143834086801056}},\n", + " '2': {'4': {'length': 0.033602086371783024},\n", + " '5': {'length': 0.11513818546516352}},\n", + " '3': {},\n", + " '4': {'6': {'length': 0.0815360990933805},\n", + " '7': {'length': 0.0815360990933805}},\n", + " '5': {},\n", + " '6': {},\n", + " '7': {}},\n", + " '_succ': {'0': {'1': {'length': 0.17454120776779053}},\n", + " '1': {'2': {'length': 0.10630015540284704},\n", + " '3': {'length': 0.22143834086801056}},\n", + " '2': {'4': {'length': 0.033602086371783024},\n", + " '5': {'length': 0.11513818546516352}},\n", + " '3': {},\n", + " '4': {'6': {'length': 0.0815360990933805},\n", + " '7': {'length': 0.0815360990933805}},\n", + " '5': {},\n", + " '6': {},\n", + " '7': {}},\n", + " '_pred': {'0': {},\n", + " '1': {'0': {'length': 0.17454120776779053}},\n", + " '2': {'1': {'length': 0.10630015540284704}},\n", + " '3': {'1': {'length': 0.22143834086801056}},\n", + " '4': {'2': {'length': 0.033602086371783024}},\n", + " '5': {'2': {'length': 0.11513818546516352}},\n", + " '6': {'4': {'length': 0.0815360990933805}},\n", + " '7': {'4': {'length': 0.0815360990933805}}},\n", + " 'nodes': NodeView(('0', '1', '2', '3', '4', '5', '6', '7')),\n", + " 'succ': AdjacencyView({'0': {'1': {'length': 0.17454120776779053}}, '1': {'2': {'length': 0.10630015540284704}, '3': {'length': 0.22143834086801056}}, '2': {'4': {'length': 0.033602086371783024}, '5': {'length': 0.11513818546516352}}, '3': {}, '4': {'6': {'length': 0.0815360990933805}, '7': {'length': 0.0815360990933805}}, '5': {}, '6': {}, '7': {}}),\n", + " 'edges': OutEdgeView([('0', '1'), ('1', '2'), ('1', '3'), ('2', '4'), ('2', '5'), ('4', '6'), ('4', '7')]),\n", + " 'adj': AdjacencyView({'0': {'1': {'length': 0.17454120776779053}}, '1': {'2': {'length': 0.10630015540284704}, '3': {'length': 0.22143834086801056}}, '2': {'4': {'length': 0.033602086371783024}, '5': {'length': 0.11513818546516352}}, '3': {}, '4': {'6': {'length': 0.0815360990933805}, '7': {'length': 0.0815360990933805}}, '5': {}, '6': {}, '7': {}}),\n", + " 'in_degree': InDegreeView({'0': 0, '1': 1, '2': 1, '3': 1, '4': 1, '5': 1, '6': 1, '7': 1}),\n", + " 'out_degree': OutDegreeView({'0': 1, '1': 2, '2': 2, '3': 0, '4': 2, '5': 0, '6': 0, '7': 0})}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ground_truth_tree.__dict__ \n", + "ground_truth_tree._CassiopeiaTree__network.__dict__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We canNOT upload and export trees to iTOL (iTOL is no longer free). Instead, we can use the local visualization library. Learn more in local_plotting.ipynb. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "cas.pl.plot_matplotlib(ground_truth_tree, add_root=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's try to annotate the tree with the copy numbers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overlay Lineage Tracing Data\n", + "\n", + "We can then use our Cas9-based lineage tracing data simulator to overlay data. Here, this is a very flexible Cas9-data generator in which we can specify the dependence structure between characters (i.e. the cassettes that link togehter cut sites), the mutation rate, silencing rates, and state distributions. This function derives from the DataSimulator class & implements the `overlay_data` function which operates on a `CassiopeiaTree`, such as the one you simulated above. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(seed=None)\n", + "lt_sim = cas.sim.Cas9LineageTracingDataSimulator(\n", + " number_of_cassettes = 40,\n", + " size_of_cassette = 1,\n", + " mutation_rate = 0.1,\n", + " state_generating_distribution = lambda: np.random.exponential(1e-5),\n", + " number_of_states = 50,\n", + " state_priors = None,\n", + " heritable_silencing_rate = 9e-4,\n", + " stochastic_silencing_rate = 0.1,\n", + " heritable_missing_data_state = -1,\n", + " stochastic_missing_data_state = -1,\n", + ")\n", + "lt_sim.overlay_data(ground_truth_tree)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can get a feel for the character matrix and the complexity of the data here by extracting the simulated data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## inspect character matrix\n", + "\n", + "character_matrix = ground_truth_tree.character_matrix\n", + "\n", + "print(f\"We simulated {character_matrix.shape[1]} characters across {character_matrix.shape[0]} cells.\")\n", + "\n", + "# plot the number of unique states per character\n", + "character_matrix.nunique(axis=0).plot(kind='bar')\n", + "plt.xlabel(\"Character\")\n", + "plt.ylabel(\"Number of unique states \")\n", + "plt.show()\n", + "\n", + "missing_data_per_character = character_matrix.apply(lambda x: len(x[x == -1]) / len(x), axis=0)\n", + "missing_data_per_character.plot(kind='bar')\n", + "plt.xlabel(\"Character\")\n", + "plt.ylabel(\"Percentage of missing states\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rebuild trees\n", + "\n", + "Cassiopeia has implemented several CassiopeiaSolvers for reconstructing trees. Each of these can take in several class-specific parameters and at a minimum implements the `solve` routine which operates on a CassiopeiaTree. \n", + "\n", + "The full list of solvers can be found [here](https://cassiopeia-lineage.readthedocs.io/en/latest/api/solver.html). For a full tutorial on tree reconstruction, refer to the [Tree Reconstruction notebook](https://github.com/YosefLab/Cassiopeia/blob/latest/notebooks/reconstruct.ipynb).\n", + "\n", + "Here we use the VanillaGreedySolver, which was described in the [Cassiopeia paper published in 2020](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02000-8)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "reconstructed_tree = cas.data.CassiopeiaTree(character_matrix = ground_truth_tree.character_matrix, missing_state_indicator = -1)\n", + "\n", + "greedy_solver = cas.solver.VanillaGreedySolver()\n", + "greedy_solver.solve(reconstructed_tree)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cas.pl.plot_matplotlib(reconstructed_tree, add_root=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Critique\n", + "\n", + "Our `critique` library is for testing the similarity between CassiopeiaTrees. The most pertinent use case is when comparing the reconstruction accuracy. We have two metrics implemented - a triplets correct measure, and the Robinson-Foulds metric." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "reconstructed_tree.collapse_mutationless_edges(infer_ancestral_characters = True)\n", + "\n", + "rf, rf_max = cas.critique.compare.robinson_foulds(ground_truth_tree, reconstructed_tree)\n", + "\n", + "print(f\"Normalized RF: {rf / rf_max}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "triplets = cas.critique.compare.triplets_correct(ground_truth_tree, reconstructed_tree, number_of_trials = 500)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building benchmarking pipelines\n", + "\n", + "We can link all of these steps together to benchmark the performance of several algorithms.\n", + "\n", + "The briefest of benchmarking pipelines would consist of:\n", + "\n", + "- Simulating a ground truth phylogeny\n", + "- Overlaying data onto this phylogeny\n", + "- Inferring the phylogeny with potentially many algorithms\n", + "- Assessing performance\n", + "\n", + "Here we show a very simple benchmarking pipeline:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "algorithms = {\"Vanilla Greedy\": cas.solver.VanillaGreedySolver(),\n", + " 'UPGMA': cas.solver.UPGMASolver(dissimilarity_function=cas.solver.dissimilarity.weighted_hamming_distance),\n", + " 'NeighborJoining': cas.solver.NeighborJoiningSolver(dissimilarity_function=cas.solver.dissimilarity.weighted_hamming_distance, add_root=True)\n", + " }\n", + "\n", + "iterations = 10\n", + "\n", + "algorithm_to_performance_triplets = defaultdict(list)\n", + "algorithm_to_performance_rf = defaultdict(list)\n", + "for _ in tqdm(range(iterations)):\n", + " \n", + " ground_truth_tree = bd_sim.simulate_tree()\n", + " \n", + " np.random.seed(None)\n", + " lt_sim.overlay_data(ground_truth_tree)\n", + " \n", + " for algorithm_name in tqdm(algorithms.keys()):\n", + " \n", + " algorithm = algorithms[algorithm_name]\n", + " \n", + " reconstructed_tree = cas.data.CassiopeiaTree(character_matrix = ground_truth_tree.character_matrix, missing_state_indicator = -1)\n", + " algorithm.solve(reconstructed_tree)\n", + " \n", + " # ground_truth_tree.collapse_mutationless_edges(infer_ancestral_characters = False)\n", + " reconstructed_tree.collapse_mutationless_edges(infer_ancestral_characters = True)\n", + "\n", + " rf, rf_max = cas.critique.compare.robinson_foulds(ground_truth_tree, reconstructed_tree)\n", + " \n", + " triplets = cas.critique.compare.triplets_correct(ground_truth_tree, reconstructed_tree, number_of_trials=500)\n", + " algorithm_to_performance_triplets[algorithm_name].append(np.mean(list(triplets[0].values())))\n", + " \n", + " algorithm_to_performance_rf[algorithm_name].append(rf / rf_max)\n", + "\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can easily visualize results after reformatting the dictionaries of performances" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "algorithm_to_performance_triplets_df = pd.DataFrame(columns = ['Algorithm', 'Triplets'])\n", + "for algorithm in algorithm_to_performance_triplets:\n", + " \n", + " entries = algorithm_to_performance_triplets[algorithm]\n", + " new_df = pd.DataFrame([algorithm]*len(entries), columns = ['Algorithm'])\n", + " new_df['Triplets'] = entries\n", + " \n", + " algorithm_to_performance_triplets_df = pd.concat([algorithm_to_performance_triplets_df, new_df])\n", + " \n", + "algorithm_to_performance_rf_df = pd.DataFrame(columns = ['Algorithm', 'RF'])\n", + "for algorithm in algorithm_to_performance_rf:\n", + " \n", + " entries = algorithm_to_performance_rf[algorithm]\n", + " new_df = pd.DataFrame([algorithm]*len(entries), columns = ['Algorithm'])\n", + " new_df['RF'] = entries\n", + " \n", + " algorithm_to_performance_rf_df = pd.concat([algorithm_to_performance_rf_df, new_df])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.boxplot(data=algorithm_to_performance_triplets_df, x = 'Algorithm', y = 'Triplets')\n", + "plt.show()\n", + "\n", + "sns.boxplot(data=algorithm_to_performance_rf_df, x = 'Algorithm', y = 'RF')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "7281e31e459af3472f32ee3e42bf55bc232e963b8420c818e7fe7d635207fd6b" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 8cec035626e3aa6fdf0349e6645746bb345e95fa Mon Sep 17 00:00:00 2001 From: jpdsilva Date: Mon, 22 Aug 2022 14:07:30 -0700 Subject: [PATCH 08/37] continuing to debug / clean up ecDNABirthDeathSimulator.py, working on neutral model tests in notebooks --- .../simulator/ecDNABirthDeathSimulator.py | 89 ++- notebooks/ecDNA_sim_test.ipynb | 540 +++++++++++++++--- 2 files changed, 502 insertions(+), 127 deletions(-) diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 03a1779c..8d21a8eb 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -98,8 +98,8 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): the tree resulting from pruning dead lineages random_seed: A seed for reproducibility initial_copy_number: Initial copy number for parental lineage. - splitting_function: As implemented, the function that describes segregation of each species at cell division. TO DO: fix this implementation to allow for non-independent segregation. - + splitting_function: As implemented, the function that describes segregation of each species at cell division. TO DO: fix this implementation to allow for non-independent segregation. + Raises: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) TreeSimulatorError if invalid stopping conditions are provided or if a fitness distribution is not provided when a mutation distribution isn't @@ -110,9 +110,7 @@ def __init__( self, birth_waiting_distribution: Callable[[float], float], initial_birth_scale: float, - death_waiting_distribution: Optional[ - Callable[[], float] - ] = lambda: np.inf, + death_waiting_distribution: Optional[Callable[[], float]] = lambda: np.inf, mutation_distribution: Optional[Callable[[], int]] = None, fitness_distribution: Optional[Callable[[], float]] = None, fitness_base: float = np.e, @@ -121,30 +119,20 @@ def __init__( collapse_unifurcations: bool = True, random_seed: int = None, initial_copy_number: np.array = np.array([1]), - splitting_function: Callable[int, int] = lambda x: np.random.binomial( - x, p=0.5 - ), + splitting_function: Callable[int, int] = lambda x: np.random.binomial(x, p=0.5), ): if num_extant is None and experiment_time is None: - raise TreeSimulatorError( - "Please specify at least one stopping condition" - ) + raise TreeSimulatorError("Please specify at least one stopping condition") if mutation_distribution is not None and fitness_distribution is None: - raise TreeSimulatorError( - "Please specify a fitness strength distribution" - ) + raise TreeSimulatorError("Please specify a fitness strength distribution") if num_extant is not None and num_extant <= 0: raise TreeSimulatorError( "Please specify number of extant lineages greater than 0" ) if num_extant is not None and type(num_extant) is not int: - raise TreeSimulatorError( - "Please specify an integer number of extant tips" - ) + raise TreeSimulatorError("Please specify an integer number of extant tips") if experiment_time is not None and experiment_time <= 0: - raise TreeSimulatorError( - "Please specify an experiment time greater than 0" - ) + raise TreeSimulatorError("Please specify an experiment time greater than 0") self.birth_waiting_distribution = birth_waiting_distribution self.initial_birth_scale = initial_birth_scale @@ -255,48 +243,41 @@ def sample_lineage_event( non-active lineage is passed in """ if not lineage["active"]: - raise TreeSimulatorError( - "Cannot sample event for non-active lineage" - ) + raise TreeSimulatorError("Cannot sample event for non-active lineage") unique_id = next(names) - birth_waiting_time = self.birth_waiting_distribution( - lineage["birth_scale"] - ) + birth_waiting_time = self.birth_waiting_distribution(lineage["birth_scale"]) death_waiting_time = self.death_waiting_distribution() if birth_waiting_time <= 0 or death_waiting_time <= 0: raise TreeSimulatorError("0 or negative waiting time detected") - # TO DO: this is a really hacky fix b/c it bypasses the length checks of whether the first birth_waiting_time exceeds self.experiment_time. Also, it just assumes the first event is a birth. we could also WOLOG that the first birth_waiting_time of the experiment is 0 (but that requires shifting times elsewhere in order to permit correct model comparison to non-ecDNA simulators. + # TO DO: this is a really hacky fix b/c it bypasses the length checks of whether the first birth_waiting_time exceeds self.experiment_time. Also, it just assumes the first event is a birth. we could also WOLOG that the first birth_waiting_time of the experiment is 0 (but that requires shifting times elsewhere in order to permit correct model comparison to non-ecDNA simulators. if lineage["total_time"] == 0: print("current_lineages is empty") print("\n") # Update birth rate - updated_birth_scale = self.update_fitness( - lineage["birth_scale"] - ) + updated_birth_scale = self.update_fitness(lineage["birth_scale"]) - #child_ecdna_array = self.get_ecdna_array(lineage["id"],tree) + # child_ecdna_array = self.get_ecdna_array(lineage["id"],tree) # Annotate parameters for a given node in the tree tree.add_node(unique_id) tree.nodes[unique_id]["birth_scale"] = updated_birth_scale tree.add_edge(lineage["id"], unique_id) - tree.nodes[unique_id]["time"] = ( - birth_waiting_time + lineage["total_time"] - ) - tree.nodes[unique_id]["ecdna_array"] = tree.nodes[lineage["id"]]["ecdna_array"] # child_ecdna_array + tree.nodes[unique_id]["time"] = birth_waiting_time + lineage["total_time"] + tree.nodes[unique_id]["ecdna_array"] = tree.nodes[lineage["id"]][ + "ecdna_array" + ] # child_ecdna_array # Add the newly generated cell to the list of living lineages current_lineages.put( ( - birth_waiting_time + lineage["total_time"], + birth_waiting_time + lineage["total_time"], unique_id, { "id": unique_id, "birth_scale": updated_birth_scale, - "total_time": birth_waiting_time - + lineage["total_time"], + "total_time": birth_waiting_time + lineage["total_time"], "active": True, }, ) @@ -307,10 +288,8 @@ def sample_lineage_event( # just cut off the living branch length at the experiment time if ( self.experiment_time - and lineage["total_time"] + birth_waiting_time - >= self.experiment_time - and lineage["total_time"] + death_waiting_time - >= self.experiment_time + and lineage["total_time"] + birth_waiting_time >= self.experiment_time + and lineage["total_time"] + death_waiting_time >= self.experiment_time ): tree.add_node(unique_id) @@ -340,11 +319,9 @@ def sample_lineage_event( else: if birth_waiting_time < death_waiting_time: # Update birth rate - updated_birth_scale = self.update_fitness( - lineage["birth_scale"] - ) + updated_birth_scale = self.update_fitness(lineage["birth_scale"]) - child_ecdna_array = self.get_ecdna_array(lineage["id"],tree) + child_ecdna_array = self.get_ecdna_array(lineage["id"], tree) # Annotate parameters for a given node in the tree tree.add_node(unique_id) @@ -362,8 +339,7 @@ def sample_lineage_event( { "id": unique_id, "birth_scale": updated_birth_scale, - "total_time": birth_waiting_time - + lineage["total_time"], + "total_time": birth_waiting_time + lineage["total_time"], "active": True, }, ) @@ -376,9 +352,9 @@ def sample_lineage_event( tree.nodes[unique_id]["time"] = ( death_waiting_time + lineage["total_time"] ) - tree.nodes[unique_id]["ecdna_array"] = tree.nodes[ - lineage["id"] - ]["ecdna_array"] + tree.nodes[unique_id]["ecdna_array"] = tree.nodes[lineage["id"]][ + "ecdna_array" + ] current_lineages.put( ( death_waiting_time + lineage["total_time"], @@ -386,8 +362,7 @@ def sample_lineage_event( { "id": unique_id, "birth_scale": lineage["birth_scale"], - "total_time": death_waiting_time - + lineage["total_time"], + "total_time": death_waiting_time + lineage["total_time"], "active": False, }, ) @@ -404,7 +379,7 @@ def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: Numpy array corresponding to the ecDNA copy numbers for the new child. """ - parental_ecdna_array = 2*tree.nodes[parent_id]["ecdna_array"] + parental_ecdna_array = 2 * tree.nodes[parent_id]["ecdna_array"] has_child = tree.out_degree(parent_id) > 0 @@ -417,9 +392,9 @@ def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: new_ecdna_array = parental_ecdna_array - child_ecdna_array else: - new_ecdna_array = np.array([ - self.splitting_function(n) for n in parental_ecdna_array - ]) + new_ecdna_array = np.array( + [self.splitting_function(n) for n in parental_ecdna_array] + ) # check that new ecdnay array entries do not exceed parental entries if np.any(new_ecdna_array > parental_ecdna_array): diff --git a/notebooks/ecDNA_sim_test.ipynb b/notebooks/ecDNA_sim_test.ipynb index 21c481d3..37917698 100644 --- a/notebooks/ecDNA_sim_test.ipynb +++ b/notebooks/ecDNA_sim_test.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -35,13 +35,20 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from IPython.display import Image\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's where I recall python syntax / test code snippets" + ] + }, { "cell_type": "code", "execution_count": null, @@ -62,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -80,9 +87,9 @@ " birth_waiting_distribution = lambda scale: np.random.exponential(scale),\n", " initial_birth_scale = 0.5,\n", " death_waiting_distribution = lambda: np.inf, # lambda: np.random.exponential(1.5),\n", - " mutation_distribution = lambda: 1 if np.random.uniform() < 0.5 else 0,\n", - " fitness_distribution = lambda: np.random.normal(0, .5),\n", - " fitness_base = 1.3,\n", + " # mutation_distribution = lambda: 1 if np.random.uniform() < 0.5 else 0,\n", + " # fitness_distribution = lambda: np.random.normal(0, .5),\n", + " # fitness_base = 1.3,\n", " num_extant = 4,\n", " random_seed=17, \n", " initial_copy_number = np.array([7,3,5]),\n", @@ -104,14 +111,12 @@ "\n", "I didn't figure out why the other condition in sample_lineage_event didn't work, but this one (checking whether we're at time 0) does. So i'll go with it for now.\n", "\n", - "I manually checked that the ecDNA CN are doubling every generation (for two generations).\n", - "\n", - "Where is the rng getting seeded? B/c it seems like it is, and in a setting where the long-time dynamics of the simulations depend heavily on the first division, it's quite important to be aware of seeding!" + "I manually checked that the ecDNA CN are doubling every generation (for two generations).\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -126,69 +131,69 @@ " 'time': 0.17454120776779053,\n", " 'ecdna_array': array([7, 3, 5]),\n", " 'character_states': []},\n", - " '2': {'birth_scale': 0.5426436139245218,\n", - " 'time': 0.28084136317063757,\n", - " 'ecdna_array': array([8, 3, 2]),\n", + " '2': {'birth_scale': 0.5,\n", + " 'time': 0.5526770979578748,\n", + " 'ecdna_array': array([5, 1, 6]),\n", " 'character_states': []},\n", " '3': {'birth_scale': 0.5,\n", - " 'time': 0.3959795486358011,\n", - " 'ecdna_array': array([6, 3, 8]),\n", + " 'time': 0.7085830237491872,\n", + " 'ecdna_array': array([9, 5, 4]),\n", " 'character_states': []},\n", - " '4': {'birth_scale': 0.5426436139245218,\n", - " 'time': 0.3144434495424206,\n", - " 'ecdna_array': array([10, 1, 2]),\n", + " '4': {'birth_scale': 0.5,\n", + " 'time': 0.7395444922982826,\n", + " 'ecdna_array': array([5, 0, 5]),\n", " 'character_states': []},\n", - " '5': {'birth_scale': 0.5426436139245218,\n", - " 'time': 0.3959795486358011,\n", - " 'ecdna_array': array([6, 5, 2]),\n", + " '5': {'birth_scale': 0.5,\n", + " 'time': 0.7395444922982826,\n", + " 'ecdna_array': array([5, 2, 7]),\n", " 'character_states': []},\n", - " '6': {'birth_scale': 0.5426436139245218,\n", - " 'time': 0.3959795486358011,\n", - " 'ecdna_array': array([9, 1, 2]),\n", + " '6': {'birth_scale': 0.5,\n", + " 'time': 0.7395444922982826,\n", + " 'ecdna_array': array([11, 7, 2]),\n", " 'character_states': []},\n", - " '7': {'birth_scale': 0.5426436139245218,\n", - " 'time': 0.3959795486358011,\n", - " 'ecdna_array': array([11, 1, 2]),\n", + " '7': {'birth_scale': 0.5,\n", + " 'time': 0.7395444922982826,\n", + " 'ecdna_array': array([7, 3, 6]),\n", " 'character_states': []}},\n", " '_adj': {'0': {'1': {'length': 0.17454120776779053}},\n", - 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" 'out_degree': OutDegreeView({'0': 1, '1': 2, '2': 2, '3': 0, '4': 2, '5': 0, '6': 0, '7': 0})}" + " 'out_degree': OutDegreeView({'0': 1, '1': 2, '2': 2, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0})}" ] }, - "execution_count": 11, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -207,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -216,13 +221,13 @@ "(
, )" ] }, - "execution_count": 10, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -262,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -291,9 +296,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We simulated 40 characters across 4 cells.\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "## inspect character matrix\n", "\n", @@ -329,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -341,9 +378,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "cas.pl.plot_matplotlib(reconstructed_tree, add_root=True)" ] @@ -359,9 +419,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized RF: 0.0\n" + ] + } + ], "source": [ "reconstructed_tree.collapse_mutationless_edges(infer_ancestral_characters = True)\n", "\n", @@ -372,9 +440,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(defaultdict(, {0: 1.0}), defaultdict(, {0: 1.0}), defaultdict(, {0: 1.0}), defaultdict(, {0: 0.0}))\n" + ] + } + ], "source": [ "triplets = cas.critique.compare.triplets_correct(ground_truth_tree, reconstructed_tree, number_of_trials = 500)" ] @@ -399,9 +475,254 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ba74d0b9517b4b329762e645306ef5c7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/10 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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uvUluH1rFI5N8vjtC+8zIe+u/dMtckeSJ3bqWJ/n77sj775I8rpt+TpKPJ7kc+NORGmdaZvgMwFe69+cVSb6d5N900+8/aunan921vSHJ64e28UdJru/ef+dNr7dPzQdBVf0QuAJY1U06Drigu1Pa27pfGj4deEGSpw8turWqns3gDbwzL9TpVXV4VT2NwZ7jr4xp81Tgm7Os55Zu+/8TeDvwom58Enhjd3rio8DLq+o5wNnAH3fLfgo4uapGP+T/HDgBIMmTgH2r6qqdeGwt+FXg6iQHAI+squ/O0v4LwK8PLfvXI/Onw+GLDHZIFu/KYndTZwCvSvLokekfAT5UVYcDvwF8csyyHwE+0h2BbRmZ9yzgDcAK4BeBI4fm3dYtczrw4W7aR4E1VfV04DPA8GmbZcAvV9UbR7axvWWG7V1VR3T1vHOGNk8GXgIcAbwzyeIk04/9GQw+k+bkl87NB0Fn+PTQ8GmhV3R73d9i8OG8YmiZv+z+3wAs34lt/bsklye5Gnhht94ZJTksg/PK30nyyqFZn+3+f25X19eTXMng1p+PB34JeBpwcTf97cCyDK4zHFhVl3bLnzu0zs8Bv9J9GP02cM5OPK6FbqbvUU9Pf3/3PJ4IvHa0UZKXdK/TjUl+eWjWLcCtSY4DrgPuGFpmH+ClwF91RxGXM/hg2KN1j/XTwOhpkhcBp3fP81rggOmj2yHPY/B3CvAXI/OuqKotVXUfcCUPfl+eN/T/9DWe5w2t41zg+UPtP1dVPx1T/vaWGbYjnw9/U1V3VdVWBqcUH8sgvL5UVXdW1f9j2x2HXvR6h7IF5EvAh5I8G9i/qjYkOYTBnv7hVXVrknOAfYeWuav7/6fs4POYZF/gY8BEVd2UwUWyfcc03cTgusAlVXU18Mzu0Hj43PNPplcLXFxVDzqtkOQwYFNVPW9k+oEz1VdVdyS5GDgWeAXwnB15XHuIW4CfHZn2c8D0Hv+bq+rzwzOT3J7kkKr6blVdBFzUHfrvM7KezzLYCz5hZPpLgAMZHGEA7A/8CzD2ouce5sMMjno/NTRtLwbXW+4cbjj+7OlYdw0Nj74va4bhmfxk9iY7VMv2Ph+2V++c8ogAqKrbgUsYnD6Z3nM4gMEfw21JHssDp44ejukP/a3dns5M3yT6E+C/JVk2NG2/GdpeBhw5dN7zEd1pneuBpem+4dIddj61O5/9oyTTezKvGlnfJxkc7q6vqlt34rEtaN3fwPeTvBAgyc8BK4GvbWexPwHOnA7X7pz0uGD/IoNzzReNTF8NvK6qllfVcuAQ4MVJ9n8YD2VB6E7JXsCDj66+DJw8PTJ8vWrIZQxOncDO3eP8lUP/f6Mb/sehdbwK+OroQmM8lGV2xteBX02yb/cZMe7U8S7nEcEDps/THgdQVVcl+RbwTwy+0fH1h7uBqvpRkk8A1wD/B1g/Q7sLkywF1mXwraAfdcuMfpBQVVNJTgDOS/Iz3eS3V9W3M/jK6mndudi9GeyFbQJ+Czg7STF48w2vb0OSH/PgPbVWvAY4I8kHu/F3V9V3trNHeibwCODyJHcBtzP4O/nWcKPuEP9UeGDvtvuwXwn87lC7nyT5GoNrCZ9lz/cB4KSh8dczeP43Mvh7vZSh56fzBuC/J3kb8LfAbTu4rZ/t1nsXD1yUPxn4VJI3A1MM3hfj7M0De+87usxDUlXrk6wFNgL/F7iaHX+MD5ldTOhBkvwC8BXgyd25Vmm30QXov1RVddddVlfVsT1v80PAP1fVx/rcztD2HllVt3eP9VLgxKqa7csjD4tHBLpfBj+I+WPgjYaAdlPPYXBBOQyOlH+7z40lWcfgms+7+tzOiLOSrGBwmnFN3yEAHhFIUvO8WCxJjTMIJKlxBoEkNc4gUHOSvCyDXjCn+4japT2wZtBT7Ipu+K1D0+3pVbslg0AtWs3gh2K7vJO3JIuq6nVVdW036a3bXUDaDRgEakr3a83nM/hF6za/TE2yf5ILklyb5Itdv1AT3bzVGfRgeU2SU4eWuT3JBzLozfV5GfQoOZHkfcB+XR9En+maL0ryiQx6tP1ykv26dXwlyYcy6O32uiSHJ/nLJP+c5L19Py9qm0Gg1hwL/G1VfRu4Jclof0q/B9xaVSuAP6Lrb6n7od2pDDoKfCZweJKXdcs8Ari8qp5RVfd3SVFVpzD48dMzq2q6K49DgTO6+0z8iAe6SwC4u+vt9uMM+r/6fQYdB56Q5DG74sFL4xgEas1q4Pxu+Hy2PT30/On5VXUNg5/6w+CmM1+pqqmqupdBF8T/tpv3UwbdTe+I71bVld3waM+Ua7v/r2bQYeD3q+ou4Abg4B1cv7TT/GWxmtF1JPdC4LCun6VFDHqiPONhrvrOGbosHme0x8n9xsy7b6TdffheVY88IlBLXg6cW1WP73r8PJhBN9PDe9tfZ9AFN903fw7rpl/B4OZES7qOAFcD/7AD27wnbdxsRguYQaCWrGbQw+ywLwBvGRr/GIPuu68F3sugt9bbqur7DO5XfAlwFbChqr60A9s8C9g4dLFY2u3Y15A0pNvbX1xVdyZ5AoNbgf5SVd09z6VJvfG8o/Rg+wOXdKdzAvyeIaA9nUcEktQ4rxFIUuMMAklqnEEgSY0zCCSpcQaBJDXu/wNXQhAgTaM95gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sns.boxplot(data=algorithm_to_performance_triplets_df, x = 'Algorithm', y = 'Triplets')\n", "plt.show()\n", @@ -486,6 +832,60 @@ "sns.boxplot(data=algorithm_to_performance_rf_df, x = 'Algorithm', y = 'RF')\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Algorithm Triplets\n", + "0 Vanilla Greedy 1.000\n", + "1 Vanilla Greedy 1.000\n", + "2 Vanilla Greedy 0.502\n", + "3 Vanilla Greedy 1.000\n", + "4 Vanilla Greedy 1.000\n", + "5 Vanilla Greedy 1.000\n", + "6 Vanilla Greedy 1.000\n", + "7 Vanilla Greedy 0.486\n", + "8 Vanilla Greedy 1.000\n", + "9 Vanilla Greedy 1.000\n", + "0 UPGMA 1.000\n", + "1 UPGMA 1.000\n", + "2 UPGMA 0.500\n", + "3 UPGMA 0.530\n", + "4 UPGMA 1.000\n", + "5 UPGMA 1.000\n", + "6 UPGMA 1.000\n", + "7 UPGMA 0.504\n", + "8 UPGMA 1.000\n", + "9 UPGMA 1.000\n", + "0 NeighborJoining 1.000\n", + "1 NeighborJoining 1.000\n", + "2 NeighborJoining 0.518\n", + "3 NeighborJoining 0.256\n", + "4 NeighborJoining 1.000\n", + "5 NeighborJoining 1.000\n", + "6 NeighborJoining 1.000\n", + "7 NeighborJoining 0.482\n", + "8 NeighborJoining 1.000\n", + "9 NeighborJoining 1.000\n" + ] + } + ], + "source": [ + "print(algorithm_to_performance_triplets_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From cfbf01e6931247844857e27d4ff4632271706f03 Mon Sep 17 00:00:00 2001 From: Jeremy P D'Silva Date: Mon, 22 Aug 2022 14:19:59 -0700 Subject: [PATCH 09/37] Delete ecDNABirthDeathSimulator_v2.py deleted unnecessary file (was just playing around with a copy of BirthDeathFitnessSimulator here, decided to go with the strategy implemented with mattjones315 in ecDNABirthDeathSimulator.py instead) --- .../simulator/ecDNABirthDeathSimulator_v2.py | 429 ------------------ 1 file changed, 429 deletions(-) delete mode 100644 cassiopeia/simulator/ecDNABirthDeathSimulator_v2.py diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator_v2.py b/cassiopeia/simulator/ecDNABirthDeathSimulator_v2.py deleted file mode 100644 index 99c19d4b..00000000 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator_v2.py +++ /dev/null @@ -1,429 +0,0 @@ -""" -This file stores a general phylogenetic tree simulator using forward birth-death -process, including differing fitness on lineages on the tree. Allows for a -variety of division and fitness regimes to be specified by the user. -""" -from typing import Callable, Dict, Generator, List, Optional, Union - -import networkx as nx -import numpy as np -from queue import PriorityQueue - -from cassiopeia.data.CassiopeiaTree import CassiopeiaTree -from cassiopeia.mixins import TreeSimulatorError -from cassiopeia.simulator.TreeSimulator import TreeSimulator - - -class BirthDeathFitnessSimulator(TreeSimulator): - """Simulator class for a general forward birth-death process with fitness. - Implements a flexible phylogenetic tree simulator using a forward birth- - death process. In this process starting from an initial root lineage, - births represent the branching of a new lineage and death represents the - cessation of an existing lineage. The process is represented as a tree, - with internal nodes representing division events, branch lengths - representing the lifetimes of individuals, and leaves representing samples - observed at the end of the experiment. - Allows any distribution on birth and death waiting times to - be specified, including constant, exponential, weibull, etc. If no death - waiting time distribution is provided, the process reduces to a Yule birth - process. Also robustly simulates differing fitness on lineages within a - simulated tree. Fitness in this context represents potential mutations that - may be acquired on a lineage that change the rate at which new members are - born. Each lineage maintains its own birth scale parameter, altered from an - initial specified experiment-wide birth scale parameter by accrued - mutations. Different fitness regimes can be specified based on user - provided distributions on how often fitness mutations occur and their - respective strengths. - There are two stopping conditions for the simulation. The first is "number - of extant nodes", which specifies the simulation to run until the first - moment a number of extant nodes exist. The second is "experiment time", - which specifies the time at which lineages are sampled. At least one of - these two stopping criteria must be provided. Both can be provided in which - case the simulation is run until one of the stopping conditions is reached. - Example use snippet: - # note that numpy uses a different parameterization of the - # exponential distribution with the scale parameter, which is 1/rate - birth_waiting_distribution = lambda scale: np.random.exponential(scale) - death_waiting_distribution = np.random.exponential(1.5) - initial_birth_scale = 0.5 - mutation_distribution = lambda: 1 if np.random.uniform() > 0.5 else 0 - fitness_distribution = lambda: np.random.uniform(-1,1) - fitness_base = 2 - bd_sim = BirthDeathFitnessSimulator( - birth_waiting_distribution, - initial_birth_scale, - death_waiting_distribution=death_waiting_distribution, - mutation_distribution=mutation_distribution, - fitness_distribution=fitness_distribution, - fitness_base=fitness_base, - num_extant=8 - ) - tree = bd_sim.simulate_tree() - Args: - birth_waiting_distribution: A function that samples waiting times - from the birth distribution. Determines how often births occur. - Must take a scale parameter as the input - initial_birth_scale: The initial scale parameter that is used at the - start of the experiment - death_waiting_distribution: A function that samples waiting times - from the death distribution. Determines how often deaths occur - mutation_distribution: A function that samples the number of - mutations that occur at a division event. If None, then no - mutations are sampled - fitness_distribution: One of the two elements in determining the - multiplicative coefficient of a fitness mutation. A function that - samples the exponential that the fitness base is raised by. - Determines the distribution of fitness mutation strengths. Must not - be None if mutation_distribution provided - fitness_base: One of the two elements in determining the - multiplicative strength of a fitness mutation. The base that is - raised by the value given by the fitness distribution. Determines - the base strength of fitness mutations. By default is e, Euler's - Constant - num_extant: Specifies the number of extant lineages existing at the - same time as a stopping condition for the experiment - experiment_time: Specifies the total time that the experiment runs as a - stopping condition for the experiment - collapse_unifurcations: Specifies whether to collapse unifurcations in - the tree resulting from pruning dead lineages - random_seed: A seed for reproducibility - Raises: - TreeSimulatorError if invalid stopping conditions are provided or if a - fitness distribution is not provided when a mutation distribution isn't - """ - - def __init__( - self, - birth_waiting_distribution: Callable[[float], float], - initial_birth_scale: float, - death_waiting_distribution: Optional[ - Callable[[], float] - ] = lambda: np.inf, - mutation_distribution: Optional[Callable[[], int]] = None, - fitness_distribution: Optional[Callable[[], float]] = None, - fitness_base: float = np.e, - num_extant: Optional[int] = None, - experiment_time: Optional[float] = None, - collapse_unifurcations: bool = True, - random_seed: int = None, - ): - if num_extant is None and experiment_time is None: - raise TreeSimulatorError( - "Please specify at least one stopping condition" - ) - if mutation_distribution is not None and fitness_distribution is None: - raise TreeSimulatorError( - "Please specify a fitness strength distribution" - ) - if num_extant is not None and num_extant <= 0: - raise TreeSimulatorError( - "Please specify number of extant lineages greater than 0" - ) - if num_extant is not None and type(num_extant) is not int: - raise TreeSimulatorError( - "Please specify an integer number of extant tips" - ) - if experiment_time is not None and experiment_time <= 0: - raise TreeSimulatorError( - "Please specify an experiment time greater than 0" - ) - - self.birth_waiting_distribution = birth_waiting_distribution - self.initial_birth_scale = initial_birth_scale - self.death_waiting_distribution = death_waiting_distribution - self.mutation_distribution = mutation_distribution - self.fitness_distribution = fitness_distribution - self.fitness_base = fitness_base - self.num_extant = num_extant - self.experiment_time = experiment_time - self.collapse_unifurcations = collapse_unifurcations - self.random_seed = random_seed - - def simulate_tree( - self, - ) -> CassiopeiaTree: - """Simulates trees from a general birth/death process with fitness. - A forward-time birth/death process is simulated by tracking a series of - lineages and sampling event waiting times for each lineage. Each - lineage draws death waiting times from the same distribution, but - maintains its own birth scale parameter that determines the shape of - its birth waiting time distribution. At each division event, fitness - mutation events are sampled, and the birth scale parameter is scaled by - their multiplicative coefficients. This updated birth scale passed - onto successors. - Returns: - A CassiopeiaTree with the tree topology initialized with the - simulated tree - Raises: - TreeSimulatorError if all lineages die before a stopping condition - """ - - def node_name_generator() -> Generator[str, None, None]: - """Generates unique node names for the tree.""" - i = 0 - while True: - yield str(i) - i += 1 - - names = node_name_generator() - - # Set the seed - if self.random_seed: - np.random.seed(self.random_seed) - - # Instantiate the implicit root - tree = nx.DiGraph() - root = next(names) - tree.add_node(root) - tree.nodes[root]["birth_scale"] = self.initial_birth_scale - tree.nodes[root]["time"] = 0 - current_lineages = PriorityQueue() - # Records the nodes that are observed at the end of the experiment - observed_nodes = [] - starting_lineage = { - "id": root, - "birth_scale": self.initial_birth_scale, - "total_time": 0, - "active": True, - } - - # Sample the waiting time until the first division - self.sample_lineage_event( - starting_lineage, current_lineages, tree, names, observed_nodes - ) - - # Perform the process until there are no active extant lineages left - while not current_lineages.empty(): - # If number of extant lineages is the stopping criterion, at the - # first instance of having n extant tips, stop the experiment - # and set the total lineage time for each lineage to be equal to - # the minimum, to produce ultrametric trees. Also, the birth_scale - # parameter of each leaf is rolled back to equal its parent's. - if self.num_extant: - if current_lineages.qsize() == self.num_extant: - remaining_lineages = [] - while not current_lineages.empty(): - _, _, lineage = current_lineages.get() - remaining_lineages.append(lineage) - min_total_time = remaining_lineages[0]["total_time"] - for lineage in remaining_lineages: - parent = list(tree.predecessors(lineage["id"]))[0] - tree.nodes[lineage["id"]]["time"] += ( - min_total_time - lineage["total_time"] - ) - tree.nodes[lineage["id"]]["birth_scale"] = tree.nodes[ - parent - ]["birth_scale"] - observed_nodes.append(lineage["id"]) - break - # Pop the minimum age lineage to simulate forward time - _, _, lineage = current_lineages.get() - # If the lineage is no longer active, just remove it from the queue. - # This represents the time at which the lineage dies. - if lineage["active"]: - # grab parental copy number from lineage - - # perform binomial sampling on each element in the array - - # sample lineage event 1 - - # sample lineage event 2 - - for _ in range(2): - self.sample_lineage_event( - lineage, current_lineages, tree, names, observed_nodes - ) - - cassiopeia_tree = CassiopeiaTree(tree=tree) - time_dictionary = {} - for i in tree.nodes: - time_dictionary[i] = tree.nodes[i]["time"] - cassiopeia_tree.set_times(time_dictionary) - - # Prune dead lineages and collapse resulting unifurcations - to_remove = list(set(cassiopeia_tree.leaves) - set(observed_nodes)) - cassiopeia_tree.remove_leaves_and_prune_lineages(to_remove) - if self.collapse_unifurcations and len(cassiopeia_tree.nodes) > 1: - cassiopeia_tree.collapse_unifurcations(source="1") - - # If only implicit root remains after pruning dead lineages, error - if len(cassiopeia_tree.nodes) == 1: - raise TreeSimulatorError( - "All lineages died before stopping condition" - ) - - return cassiopeia_tree - - def sample_lineage_event( - self, - lineage: Dict[str, Union[int, float]], - current_lineages: PriorityQueue, - tree: nx.DiGraph, - names: Generator, - observed_nodes: List[str], - daughter_cn, - ) -> None: - """A helper function that samples an event for a lineage. - Takes a lineage and determines the next event in that lineage's - future. Simulates the lifespan of a new descendant. Birth and - death waiting times are sampled, representing how long the - descendant lived. If a death event occurs first, then the lineage - with the new descendant is added to the queue of currently alive, - but its status is marked as inactive and will be removed at the - time the lineage dies. If a birth event occurs first, then the - lineage with the new descendant is added to the queue, but with its - status marked as active, and further events will be sampled at the - time the lineage divides. Additionally, its fitness will be updated - by altering its birth rate. The descendant node is added to the - tree object, with the edge weight between the current node and the - descendant representing the lifespan of the descendant. In the - case the descendant would live past the end of the experiment (both - birth and death times exceed past the end of the experiment), then - the lifespan is cut off at the experiment time and a final observed - sample is added to the tree. In this case the lineage is marked as - inactive as well. - Args: - unique_id: The unique ID number to be used to name a new node - added to the tree - lineage: The current extant lineage to extend. Contains the ID - of the internal node to attach the descendant to, the - current birth scale parameter of the lineage, the current - total lived time of the lineage, and the status of whether - the lineage is still dividing - current_lineages: The queue containing currently alive lineages - tree: The tree object being constructed by the simulator - representing the birth death process - names: A generator providing unique names for tree nodes - observed_nodes: A list of nodes that are observed at the end of - the experiment - Raises: - TreeSimulatorError if a negative waiting time is sampled or a - non-active lineage is passed in - """ - if not lineage["active"]: - raise TreeSimulatorError( - "Cannot sample event for non-active lineage" - ) - - unique_id = next(names) - - # @JP: update to use daughter_CN too. - birth_waiting_time = self.birth_waiting_distribution( - lineage["birth_scale"] - ) - death_waiting_time = self.death_waiting_distribution() - if birth_waiting_time <= 0 or death_waiting_time <= 0: - raise TreeSimulatorError("0 or negative waiting time detected") - - # If birth and death would happen after the total experiment time, - # just cut off the living branch length at the experiment time - if ( - self.experiment_time - and lineage["total_time"] + birth_waiting_time - >= self.experiment_time - and lineage["total_time"] + death_waiting_time - >= self.experiment_time - ): - tree.add_node(unique_id) - tree.nodes[unique_id]["birth_scale"] = lineage["birth_scale"] - tree.add_edge(lineage["id"], unique_id) - tree.nodes[unique_id]["time"] = self.experiment_time - - current_lineages.put( - ( - self.experiment_time, - unique_id, - { - "id": unique_id, - "birth_scale": lineage["birth_scale"], - "total_time": self.experiment_time, - "active": False, - }, - ) - ) - - # Indicate this node is observed at the end of experiment - observed_nodes.append(unique_id) - - else: - if birth_waiting_time < death_waiting_time: - # Update birth rate - updated_birth_scale = self.update_fitness( - lineage["birth_scale"] - ) - - # Annotate parameters for a given node in the tree - tree.add_node(unique_id) - tree.nodes[unique_id]["birth_scale"] = updated_birth_scale - tree.add_edge(lineage["id"], unique_id) - tree.nodes[unique_id]["time"] = ( - birth_waiting_time + lineage["total_time"] - ) - # Add the newly generated cell to the list of living lineages - current_lineages.put( - ( - birth_waiting_time + lineage["total_time"], - unique_id, - { - "id": unique_id, - "birth_scale": updated_birth_scale, - "total_time": birth_waiting_time - + lineage["total_time"], - "active": True, - }, - ) - ) - - else: - tree.add_node(unique_id) - tree.nodes[unique_id]["birth_scale"] = lineage["birth_scale"] - tree.add_edge(lineage["id"], unique_id) - tree.nodes[unique_id]["time"] = ( - death_waiting_time + lineage["total_time"] - ) - current_lineages.put( - ( - death_waiting_time + lineage["total_time"], - unique_id, - { - "id": unique_id, - "birth_scale": lineage["birth_scale"], - "total_time": death_waiting_time - + lineage["total_time"], - "active": False, - }, - ) - ) - - def update_fitness(self, birth_scale: float) -> float: - """Updates a lineage birth scale, which represents its fitness. - At each division event, the fitness is updated by sampling from a - distribution determining the number of mutations. The birth scale - parameter of the lineage is then scaled by the total multiplicative - coefficient across all mutations and passed on to the descendant - nodes. The multiplicative factor of each mutation is determined by - exponentiating a base parameter by a value drawn from another - 'fitness' distribution. Therefore, negative values from the fitness - distribution are valid and down-scale the birth scale parameter. - The base determines the base strength of the mutations in either - direction and the fitness distribution determines how the mutations - are distributed. - Args: - birth_scale: The birth_scale to be updated - Returns: - The updated birth_scale - Raises: - TreeSimulatorError if a negative number of mutations is sampled - """ - base_selection_coefficient = 1 - if self.mutation_distribution: - num_mutations = int(self.mutation_distribution()) - if num_mutations < 0: - raise TreeSimulatorError( - "Negative number of mutations detected" - ) - for _ in range(num_mutations): - base_selection_coefficient *= ( - self.fitness_base ** self.fitness_distribution() - ) - return birth_scale * base_selection_coefficient \ No newline at end of file From d4f79ba6f6698d1af1a47f80248ee55e2210504d Mon Sep 17 00:00:00 2001 From: jpdsilva Date: Tue, 23 Aug 2022 20:07:10 -0700 Subject: [PATCH 10/37] added unit tests for ecDNABirthDeathSimulator.py updated documentation --- .../simulator/ecDNABirthDeathSimulator.py | 4 +- .../ecdna_birth_death_simulator_test.py | 103 ++++++++++++++++-- 2 files changed, 94 insertions(+), 13 deletions(-) diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 8d21a8eb..42a0dd9b 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -76,7 +76,7 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): initial_birth_scale: The initial scale parameter that is used at the start of the experiment death_waiting_distribution: A function that samples waiting times - from the death distribution. Determines how often deaths occur + from the death distribution. Determines how often deaths occur. Default is no-death. mutation_distribution: A function that samples the number of mutations that occur at a division event. If None, then no mutations are sampled @@ -254,8 +254,6 @@ def sample_lineage_event( # TO DO: this is a really hacky fix b/c it bypasses the length checks of whether the first birth_waiting_time exceeds self.experiment_time. Also, it just assumes the first event is a birth. we could also WOLOG that the first birth_waiting_time of the experiment is 0 (but that requires shifting times elsewhere in order to permit correct model comparison to non-ecDNA simulators. if lineage["total_time"] == 0: - print("current_lineages is empty") - print("\n") # Update birth rate updated_birth_scale = self.update_fitness(lineage["birth_scale"]) diff --git a/test/simulator_tests/ecdna_birth_death_simulator_test.py b/test/simulator_tests/ecdna_birth_death_simulator_test.py index d43d6b5d..6d8e8cc8 100644 --- a/test/simulator_tests/ecdna_birth_death_simulator_test.py +++ b/test/simulator_tests/ecdna_birth_death_simulator_test.py @@ -8,7 +8,9 @@ import networkx as nx import numpy as np -from typing import List, Tuple +from typing import List, Tuple, Generator +from queue import PriorityQueue + from cassiopeia.data.CassiopeiaTree import CassiopeiaTree from cassiopeia.mixins import ecDNABirthDeathSimulatorError @@ -50,32 +52,113 @@ def extract_tree_statistics( class ecDNABirthDeathSimulatorTest(unittest.TestCase): - def test_ecdna_splitting(self): - + np.random.seed(41) sim = ecDNABirthDeathSimulator(lambda _: 1, 1, num_extant=16) tree = nx.DiGraph() tree.add_node(0) - tree.nodes[0]['ecdna_array'] = np.array([4, 5]) + tree.nodes[0]["ecdna_array"] = np.array([4, 5]) - # test new generation of child + # test new generation of child new_ecdna_array = sim.get_ecdna_array(0, tree) - expected_array = [0, 0] # manually find out and set + expected_array = [3, 2] # manually find out and set self.assertTrue(np.all(expected_array == new_ecdna_array)) # test generation when child already exists tree.add_edge(0, 1) - tree.nodes[1]['ecdna_array'] = np.array([5, 7]) + tree.nodes[1]["ecdna_array"] = np.array([5, 7]) new_ecdna_array = sim.get_ecdna_array(0, tree) - expected_array = [3, 3] # manually find out and set + expected_array = [3, 3] # calculated manually as [4*2-5, 5*2 - 7] self.assertTrue(np.all(expected_array == new_ecdna_array)) + def test_sample_lineage_event(self): + """Checks that sample_lineage_event behaves as expected under edge cases""" + np.random.seed(41) + sim = ecDNABirthDeathSimulator( + birth_waiting_distribution=lambda _: 1, + initial_birth_scale=1, + num_extant=16, + experiment_time=5, + ) + + def node_name_generator() -> Generator[str, None, None]: + """Generates unique node names for the tree.""" + i = 0 + while True: + yield str(i) + i += 1 + + # first, we'll trigger the time=0 condition in sample_lineage_event. + names = node_name_generator() - - + tree = nx.DiGraph() + root = next(names) + tree.add_node(root) + tree.nodes[root]["birth_scale"] = 1 + tree.nodes[root]["time"] = 0 + tree.nodes[root]["ecdna_array"] = np.array([3, 2, 5]) + current_lineages = PriorityQueue() + observed_nodes = [] + starting_lineage = { + "id": root, + "birth_scale": tree.nodes[root]["birth_scale"], + "total_time": tree.nodes[root]["time"], + "active": True, + } + + sim.sample_lineage_event( + starting_lineage, current_lineages, tree, names, observed_nodes + ) + self.assertTrue(current_lineages.qsize() == 1) + + _, _, new_lineage = current_lineages.get() + self.assertTrue( + np.all( + tree.nodes[root]["ecdna_array"] + == tree.nodes[new_lineage["id"]]["ecdna_array"] + ) + ) + self.assertTrue(new_lineage["active"]) + self.assertTrue( + new_lineage["total_time"] > 0 + ) # does this really need to be true? yes + + # now, let's do one normal division + sim.sample_lineage_event( + new_lineage, current_lineages, tree, names, observed_nodes + ) + # checked: the first ecdna_array is [2,0,6], so the other one should be [4,4,4] + sim.sample_lineage_event( + new_lineage, current_lineages, tree, names, observed_nodes + ) + + _, _, new_lineage = current_lineages.get() + total_time_d1 = new_lineage["total_time"] + + _, _, new_lineage = current_lineages.get() + total_time_d2 = new_lineage["total_time"] + + # This need not be true in general, it's just b/c our birth_waiting_distribution is constant. + self.assertTrue(total_time_d1 == total_time_d2) + + expected_array = [4, 4, 4] + self.assertTrue( + np.all(tree.nodes[new_lineage["id"]]["ecdna_array"] == expected_array) + ) + + # Now, let's edit new_lineage to ensure it triggers stopping condition, then pass it into sample_lineage_event + new_lineage["total_time"] = 4.5 + sim.sample_lineage_event( + new_lineage, current_lineages, tree, names, observed_nodes + ) + + _, _, new_lineage = current_lineages.get() + self.assertTrue(observed_nodes[0] == new_lineage["id"]) + self.assertTrue(new_lineage["total_time"] == 5) + self.assertTrue(not (new_lineage["active"])) if __name__ == "__main__": From 6bf1d3fe7917584094191e98e7ba47599963fe1d Mon Sep 17 00:00:00 2001 From: jpdsilva Date: Fri, 26 Aug 2022 10:28:11 -0700 Subject: [PATCH 11/37] added CN-depdt fitness with epistasis --- .../simulator/ecDNABirthDeathSimulator.py | 44 +- notebooks/ecDNA_sim_test.ipynb | 523 +++--------------- 2 files changed, 88 insertions(+), 479 deletions(-) diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 42a0dd9b..9ba1eeff 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -120,6 +120,7 @@ def __init__( random_seed: int = None, initial_copy_number: np.array = np.array([1]), splitting_function: Callable[int, int] = lambda x: np.random.binomial(x, p=0.5), + fitness_array: np.array = np.array([0,1]), ): if num_extant is None and experiment_time is None: raise TreeSimulatorError("Please specify at least one stopping condition") @@ -146,6 +147,7 @@ def __init__( self.random_seed = random_seed self.initial_copy_number = initial_copy_number self.splitting_function = splitting_function + self.fitness_array = fitness_array # update to store cn_array in node. (tree.nodes[root]["cn_array"]) def initialize_tree(self, names) -> nx.DiGraph: @@ -160,23 +162,13 @@ def initialize_tree(self, names) -> nx.DiGraph: return tree, root # update to compute fitness using lineage cn_array - def update_fitness(self, birth_scale: float) -> float: - """Updates a lineage birth scale, which represents its fitness. - - At each division event, the fitness is updated by sampling from a - distribution determining the number of mutations. The birth scale - parameter of the lineage is then scaled by the total multiplicative - coefficient across all mutations and passed on to the descendant - nodes. The multiplicative factor of each mutation is determined by - exponentiating a base parameter by a value drawn from another - 'fitness' distribution. Therefore, negative values from the fitness - distribution are valid and down-scale the birth scale parameter. - The base determines the base strength of the mutations in either - direction and the fitness distribution determines how the mutations - are distributed. + def update_fitness(self, ecdna_array: np.array) -> float: + """Updates a lineage birth scale, which represents its (Malthusian) fitness. + + Fitness is computed as a function of copy number, using the fitness_array (which defines fitness for CN=0 or CN >0 for each species, with epistasis). Args: - birth_scale: The birth_scale to be updated + ecdna_array: The birth_scale to be updated Returns: The updated birth_scale @@ -184,18 +176,8 @@ def update_fitness(self, birth_scale: float) -> float: Raises: TreeSimulatorError if a negative number of mutations is sampled """ - base_selection_coefficient = 1 - if self.mutation_distribution: - num_mutations = int(self.mutation_distribution()) - if num_mutations < 0: - raise ecDNABirthDeathSimulatorError( - "Negative number of mutations detected" - ) - for _ in range(num_mutations): - base_selection_coefficient *= ( - self.fitness_base ** self.fitness_distribution() - ) - return birth_scale * base_selection_coefficient + + return self.initial_birth_scale * (1.0 + self.fitness_array[tuple((ecdna_array > 0).astype(int))]) def sample_lineage_event( self, @@ -255,7 +237,9 @@ def sample_lineage_event( # TO DO: this is a really hacky fix b/c it bypasses the length checks of whether the first birth_waiting_time exceeds self.experiment_time. Also, it just assumes the first event is a birth. we could also WOLOG that the first birth_waiting_time of the experiment is 0 (but that requires shifting times elsewhere in order to permit correct model comparison to non-ecDNA simulators. if lineage["total_time"] == 0: # Update birth rate - updated_birth_scale = self.update_fitness(lineage["birth_scale"]) + updated_birth_scale = self.update_fitness(tree.nodes[lineage["id"]][ + "ecdna_array" + ]) # child_ecdna_array = self.get_ecdna_array(lineage["id"],tree) @@ -317,7 +301,9 @@ def sample_lineage_event( else: if birth_waiting_time < death_waiting_time: # Update birth rate - updated_birth_scale = self.update_fitness(lineage["birth_scale"]) + updated_birth_scale = self.update_fitness(tree.nodes[lineage["id"]][ + "ecdna_array" + ]) child_ecdna_array = self.get_ecdna_array(lineage["id"], tree) diff --git a/notebooks/ecDNA_sim_test.ipynb b/notebooks/ecDNA_sim_test.ipynb index 37917698..78f7c59c 100644 --- a/notebooks/ecDNA_sim_test.ipynb +++ b/notebooks/ecDNA_sim_test.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -51,9 +51,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "inf" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.inf" ] @@ -69,19 +80,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current_lineages is empty\n", - "\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "bd_sim = cas.sim.ecDNABirthDeathSimulator(\n", " birth_waiting_distribution = lambda scale: np.random.exponential(scale),\n", @@ -94,6 +95,7 @@ " random_seed=17, \n", " initial_copy_number = np.array([7,3,5]),\n", " splitting_function = lambda x: np.random.binomial(x, p=0.5),\n", + " fitness_array = np.array([[[0,0.1], [0.1,0.2]],[[0.1,0.5],[0.2,0.6]]])\n", ")\n", "ground_truth_tree = bd_sim.simulate_tree()" ] @@ -116,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -127,73 +129,73 @@ " 'time': 0,\n", " 'ecdna_array': array([7, 3, 5]),\n", " 'character_states': []},\n", - " '1': {'birth_scale': 0.5,\n", + " '1': {'birth_scale': 0.8,\n", " 'time': 0.17454120776779053,\n", " 'ecdna_array': array([7, 3, 5]),\n", " 'character_states': []},\n", - " '2': {'birth_scale': 0.5,\n", - " 'time': 0.5526770979578748,\n", + " '2': {'birth_scale': 0.8,\n", + " 'time': 0.7795586320719254,\n", " 'ecdna_array': array([5, 1, 6]),\n", " 'character_states': []},\n", - " '3': {'birth_scale': 0.5,\n", - " 'time': 0.7085830237491872,\n", + " '3': {'birth_scale': 0.8,\n", + " 'time': 1.0290081133380253,\n", " 'ecdna_array': array([9, 5, 4]),\n", " 'character_states': []},\n", - " '4': {'birth_scale': 0.5,\n", - " 'time': 0.7395444922982826,\n", + " '4': {'birth_scale': 0.8,\n", + " 'time': 1.0785464630165777,\n", " 'ecdna_array': array([5, 0, 5]),\n", " 'character_states': []},\n", - " '5': {'birth_scale': 0.5,\n", - " 'time': 0.7395444922982826,\n", + " '5': {'birth_scale': 0.8,\n", + " 'time': 1.0785464630165777,\n", " 'ecdna_array': array([5, 2, 7]),\n", " 'character_states': []},\n", - " '6': {'birth_scale': 0.5,\n", - " 'time': 0.7395444922982826,\n", + " '6': {'birth_scale': 0.8,\n", + " 'time': 1.0785464630165777,\n", " 'ecdna_array': array([11, 7, 2]),\n", " 'character_states': []},\n", - " '7': {'birth_scale': 0.5,\n", - " 'time': 0.7395444922982826,\n", + " '7': {'birth_scale': 0.8,\n", + " 'time': 1.0785464630165777,\n", " 'ecdna_array': array([7, 3, 6]),\n", " 'character_states': []}},\n", " '_adj': {'0': {'1': {'length': 0.17454120776779053}},\n", - " '1': {'2': {'length': 0.37813589019008426},\n", - " '3': {'length': 0.5340418159813967}},\n", - " '2': {'4': {'length': 0.18686739434040778},\n", - " '5': {'length': 0.18686739434040778}},\n", - " '3': {'6': {'length': 0.030961468549095317},\n", - " '7': {'length': 0.030961468549095317}},\n", + " '1': {'2': {'length': 0.6050174243041349},\n", + " '3': {'length': 0.8544669055702347}},\n", + " '2': {'4': {'length': 0.2989878309446523},\n", + " '5': {'length': 0.2989878309446523}},\n", + " '3': {'6': {'length': 0.04953834967855242},\n", + " '7': {'length': 0.04953834967855242}},\n", " '4': {},\n", " '5': {},\n", " '6': {},\n", " '7': {}},\n", " '_succ': {'0': {'1': {'length': 0.17454120776779053}},\n", - 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" 'succ': AdjacencyView({'0': {'1': {'length': 0.17454120776779053}}, '1': {'2': {'length': 0.37813589019008426}, '3': {'length': 0.5340418159813967}}, '2': {'4': {'length': 0.18686739434040778}, '5': {'length': 0.18686739434040778}}, '3': {'6': {'length': 0.030961468549095317}, '7': {'length': 0.030961468549095317}}, '4': {}, '5': {}, '6': {}, '7': {}}),\n", + " 'succ': AdjacencyView({'0': {'1': {'length': 0.17454120776779053}}, '1': {'2': {'length': 0.6050174243041349}, '3': {'length': 0.8544669055702347}}, '2': {'4': {'length': 0.2989878309446523}, '5': {'length': 0.2989878309446523}}, '3': {'6': {'length': 0.04953834967855242}, '7': {'length': 0.04953834967855242}}, '4': {}, '5': {}, '6': {}, '7': {}}),\n", " 'edges': OutEdgeView([('0', '1'), ('1', '2'), ('1', '3'), ('2', '4'), ('2', '5'), ('3', '6'), ('3', '7')]),\n", - " 'adj': AdjacencyView({'0': {'1': {'length': 0.17454120776779053}}, '1': {'2': {'length': 0.37813589019008426}, '3': {'length': 0.5340418159813967}}, '2': {'4': {'length': 0.18686739434040778}, '5': {'length': 0.18686739434040778}}, '3': {'6': {'length': 0.030961468549095317}, '7': {'length': 0.030961468549095317}}, '4': {}, '5': {}, '6': {}, '7': {}}),\n", + " 'adj': AdjacencyView({'0': {'1': {'length': 0.17454120776779053}}, '1': {'2': {'length': 0.6050174243041349}, '3': {'length': 0.8544669055702347}}, '2': {'4': {'length': 0.2989878309446523}, '5': {'length': 0.2989878309446523}}, '3': {'6': {'length': 0.04953834967855242}, '7': {'length': 0.04953834967855242}}, '4': {}, '5': {}, '6': {}, '7': {}}),\n", " 'in_degree': InDegreeView({'0': 0, '1': 1, '2': 1, '3': 1, '4': 1, '5': 1, '6': 1, '7': 1}),\n", " 'out_degree': OutDegreeView({'0': 1, '1': 2, '2': 2, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0})}" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -212,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -221,13 +223,13 @@ "(
, )" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -267,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -296,41 +298,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "We simulated 40 characters across 4 cells.\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "## inspect character matrix\n", "\n", @@ -366,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -378,32 +348,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
, )" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cas.pl.plot_matplotlib(reconstructed_tree, add_root=True)" ] @@ -419,17 +366,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Normalized RF: 0.0\n" - ] - } - ], + "outputs": [], "source": [ "reconstructed_tree.collapse_mutationless_edges(infer_ancestral_characters = True)\n", "\n", @@ -440,17 +379,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(defaultdict(, {0: 1.0}), defaultdict(, {0: 1.0}), defaultdict(, {0: 1.0}), defaultdict(, {0: 0.0}))\n" - ] - } - ], + "outputs": [], "source": [ "triplets = cas.critique.compare.triplets_correct(ground_truth_tree, reconstructed_tree, number_of_trials = 500)" ] @@ -475,254 +406,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ba74d0b9517b4b329762e645306ef5c7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/10 [00:00" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sns.boxplot(data=algorithm_to_performance_triplets_df, x = 'Algorithm', y = 'Triplets')\n", "plt.show()\n", @@ -835,47 +496,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Algorithm Triplets\n", - "0 Vanilla Greedy 1.000\n", - "1 Vanilla Greedy 1.000\n", - "2 Vanilla Greedy 0.502\n", - "3 Vanilla Greedy 1.000\n", - "4 Vanilla Greedy 1.000\n", - "5 Vanilla Greedy 1.000\n", - "6 Vanilla Greedy 1.000\n", - "7 Vanilla Greedy 0.486\n", - "8 Vanilla Greedy 1.000\n", - "9 Vanilla Greedy 1.000\n", - "0 UPGMA 1.000\n", - "1 UPGMA 1.000\n", - "2 UPGMA 0.500\n", - "3 UPGMA 0.530\n", - "4 UPGMA 1.000\n", - "5 UPGMA 1.000\n", - "6 UPGMA 1.000\n", - "7 UPGMA 0.504\n", - "8 UPGMA 1.000\n", - "9 UPGMA 1.000\n", - "0 NeighborJoining 1.000\n", - "1 NeighborJoining 1.000\n", - "2 NeighborJoining 0.518\n", - "3 NeighborJoining 0.256\n", - "4 NeighborJoining 1.000\n", - "5 NeighborJoining 1.000\n", - "6 NeighborJoining 1.000\n", - "7 NeighborJoining 0.482\n", - "8 NeighborJoining 1.000\n", - "9 NeighborJoining 1.000\n" - ] - } - ], + "outputs": [], "source": [ "print(algorithm_to_performance_triplets_df)" ] From e48163b92e8cb7d71fd3703194697f87da23eccc Mon Sep 17 00:00:00 2001 From: Matthew Gregory Jones Date: Fri, 26 Aug 2022 11:37:52 -0700 Subject: [PATCH 12/37] small notes indicating where we want to make changes --- cassiopeia/simulator/ecDNABirthDeathSimulator.py | 11 ++++++++++- .../ecdna_birth_death_simulator_test.py | 1 + 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 9ba1eeff..740d7a52 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -119,7 +119,7 @@ def __init__( collapse_unifurcations: bool = True, random_seed: int = None, initial_copy_number: np.array = np.array([1]), - splitting_function: Callable[int, int] = lambda x: np.random.binomial(x, p=0.5), + splitting_function: Callable[[int], int] = lambda x: np.random.binomial(x, p=0.5), fitness_array: np.array = np.array([0,1]), ): if num_extant is None and experiment_time is None: @@ -387,3 +387,12 @@ def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: ) return new_ecdna_array + + def populate_tree_from_simulation(self, tree: nx.DiGraph) -> CassiopeiaTree: + """Populates tree with appropriate meta data""" + + cas_tree = CassiopeiaTree(tree=tree) + + # add ecDNA (and optionally fitness of nodes) to tree as attributes + # make sure you add ecDNA as a pandas dataframe and pass it as cell_meta. + return cas_tree \ No newline at end of file diff --git a/test/simulator_tests/ecdna_birth_death_simulator_test.py b/test/simulator_tests/ecdna_birth_death_simulator_test.py index 6d8e8cc8..318dac36 100644 --- a/test/simulator_tests/ecdna_birth_death_simulator_test.py +++ b/test/simulator_tests/ecdna_birth_death_simulator_test.py @@ -82,6 +82,7 @@ def test_sample_lineage_event(self): initial_birth_scale=1, num_extant=16, experiment_time=5, + fitness_array = np.array([[[0,0.1], [0.1,0.2]],[[0.1,0.5],[0.2,0.6]]]), ) def node_name_generator() -> Generator[str, None, None]: From 87638d104e8ac61aa8e7b189b558fb6f111371b7 Mon Sep 17 00:00:00 2001 From: Matthew Gregory Jones Date: Wed, 31 Aug 2022 13:29:23 -0700 Subject: [PATCH 13/37] implemented populate tree from simulation --- .../simulator/BirthDeathFitnessSimulator.py | 23 ++++++++++++---- .../simulator/ecDNABirthDeathSimulator.py | 27 ++++++++++++++++--- 2 files changed, 41 insertions(+), 9 deletions(-) diff --git a/cassiopeia/simulator/BirthDeathFitnessSimulator.py b/cassiopeia/simulator/BirthDeathFitnessSimulator.py index c04965cb..35f15457 100644 --- a/cassiopeia/simulator/BirthDeathFitnessSimulator.py +++ b/cassiopeia/simulator/BirthDeathFitnessSimulator.py @@ -273,7 +273,8 @@ def node_name_generator() -> Generator[str, None, None]: lineage, current_lineages, tree, names, observed_nodes ) - cassiopeia_tree = CassiopeiaTree(tree=tree) + cassiopeia_tree = self.populate_tree_from_simulation(tree) + time_dictionary = {} for i in tree.nodes: time_dictionary[i] = tree.nodes[i]["time"] @@ -468,10 +469,9 @@ def update_fitness(self, birth_scale: float) -> float: self.fitness_base ** self.fitness_distribution() ) return birth_scale * base_selection_coefficient - - # should this be a static method? - @staticmethod + def make_lineage_dict( + self, id_value, birth_scale, total_time, @@ -493,4 +493,17 @@ def make_lineage_dict( "total_time": total_time, "active": active_flag, } - return lineage \ No newline at end of file + return lineage + + def populate_tree_from_simulation(self, tree: nx.DiGraph) -> CassiopeiaTree: + """Populates tree with appropriate meta data. + + Args: + tree: The tree simulated with ecDNA and fitness values populated as attributes. + + Returns: + A CassiopeiaTree with node attributes filled in. + """ + + cas_tree = CassiopeiaTree(tree=tree) + return cas_tree \ No newline at end of file diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 740d7a52..4f956ff0 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -9,6 +9,7 @@ import numpy as np from queue import PriorityQueue +from cassiopeia.data.CassiopeiaTree import CassiopeiaTree from cassiopeia.mixins import ecDNABirthDeathSimulatorError from cassiopeia.simulator.BirthDeathFitnessSimulator import ( BirthDeathFitnessSimulator, @@ -234,7 +235,9 @@ def sample_lineage_event( if birth_waiting_time <= 0 or death_waiting_time <= 0: raise TreeSimulatorError("0 or negative waiting time detected") - # TO DO: this is a really hacky fix b/c it bypasses the length checks of whether the first birth_waiting_time exceeds self.experiment_time. Also, it just assumes the first event is a birth. we could also WOLOG that the first birth_waiting_time of the experiment is 0 (but that requires shifting times elsewhere in order to permit correct model comparison to non-ecDNA simulators. + # TO DO: this is a really hacky fix b/c it bypasses the length checks of whether the first birth_waiting_time exceeds self.experiment_time. + # Also, it just assumes the first event is a birth. we could also WOLOG that the first birth_waiting_time of the experiment is 0 + # (but that requires shifting times elsewhere in order to permit correct model comparison to non-ecDNA simulators. if lineage["total_time"] == 0: # Update birth rate updated_birth_scale = self.update_fitness(tree.nodes[lineage["id"]][ @@ -389,10 +392,26 @@ def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: return new_ecdna_array def populate_tree_from_simulation(self, tree: nx.DiGraph) -> CassiopeiaTree: - """Populates tree with appropriate meta data""" + """Populates tree with appropriate meta data. + + Args: + tree: The tree simulated with ecDNA and fitness values populated as attributes. + + Returns: + A CassiopeiaTree with node attributes filled in. + """ cas_tree = CassiopeiaTree(tree=tree) - # add ecDNA (and optionally fitness of nodes) to tree as attributes - # make sure you add ecDNA as a pandas dataframe and pass it as cell_meta. + for node in cas_tree.depth_first_traverse_nodes(): + + cas_tree.set_attribute(node, 'ecdna_array', tree.nodes[node]['ecdna_array']) + cas_tree.set_attribute(node, 'fitness', tree.nodes[node]['birth_scale']) + + cell_metadata = pd.DataFrame(columns = ['ecDNA{i}' for i in len(self.initial_copy_number)]) + for leaf in cas_tree.leaves: + cell_metadata.loc[leaf] = cas_tree.get_attribute(leaf, 'ecdna_array') + + cas_tree.cell_meta = cell_metadata.copy() + return cas_tree \ No newline at end of file From 1689f4a59ff35129c17f0eef022c0a19e7ba695a Mon Sep 17 00:00:00 2001 From: Matthew Gregory Jones Date: Wed, 31 Aug 2022 16:30:40 -0700 Subject: [PATCH 14/37] wrote more tests --- .../simulator/BirthDeathFitnessSimulator.py | 46 ++++---- .../simulator/ecDNABirthDeathSimulator.py | 44 +++++-- notebooks/benchmark.ipynb | 4 +- notebooks/ecDNA_sim_test.ipynb | 2 +- .../ecdna_birth_death_simulator_test.py | 107 +++++++++++++++--- 5 files changed, 149 insertions(+), 54 deletions(-) diff --git a/cassiopeia/simulator/BirthDeathFitnessSimulator.py b/cassiopeia/simulator/BirthDeathFitnessSimulator.py index 35f15457..730dbe35 100644 --- a/cassiopeia/simulator/BirthDeathFitnessSimulator.py +++ b/cassiopeia/simulator/BirthDeathFitnessSimulator.py @@ -273,24 +273,7 @@ def node_name_generator() -> Generator[str, None, None]: lineage, current_lineages, tree, names, observed_nodes ) - cassiopeia_tree = self.populate_tree_from_simulation(tree) - - time_dictionary = {} - for i in tree.nodes: - time_dictionary[i] = tree.nodes[i]["time"] - cassiopeia_tree.set_times(time_dictionary) - - # Prune dead lineages and collapse resulting unifurcations - to_remove = list(set(cassiopeia_tree.leaves) - set(observed_nodes)) - cassiopeia_tree.remove_leaves_and_prune_lineages(to_remove) - if self.collapse_unifurcations and len(cassiopeia_tree.nodes) > 1: - cassiopeia_tree.collapse_unifurcations(source="1") - - # If only implicit root remains after pruning dead lineages, error - if len(cassiopeia_tree.nodes) == 1: - raise TreeSimulatorError( - "All lineages died before stopping condition" - ) + cassiopeia_tree = self.populate_tree_from_simulation(tree, observed_nodes) return cassiopeia_tree @@ -495,15 +478,34 @@ def make_lineage_dict( } return lineage - def populate_tree_from_simulation(self, tree: nx.DiGraph) -> CassiopeiaTree: + def populate_tree_from_simulation(self, tree: nx.DiGraph, observed_nodes: List[str]) -> CassiopeiaTree: """Populates tree with appropriate meta data. Args: tree: The tree simulated with ecDNA and fitness values populated as attributes. + observed_nodes: The observed leaves of the tree. Returns: - A CassiopeiaTree with node attributes filled in. + A CassiopeiaTree with relevant node attributes filled in. """ - cas_tree = CassiopeiaTree(tree=tree) - return cas_tree \ No newline at end of file + cassiopeia_tree = CassiopeiaTree(tree=tree) + + time_dictionary = {} + for i in tree.nodes: + time_dictionary[i] = tree.nodes[i]["time"] + cassiopeia_tree.set_times(time_dictionary) + + # Prune dead lineages and collapse resulting unifurcations + to_remove = list(set(cassiopeia_tree.leaves) - set(observed_nodes)) + cassiopeia_tree.remove_leaves_and_prune_lineages(to_remove) + if self.collapse_unifurcations and len(cassiopeia_tree.nodes) > 1: + cassiopeia_tree.collapse_unifurcations(source="1") + + # If only implicit root remains after pruning dead lineages, error + if len(cassiopeia_tree.nodes) == 1: + raise TreeSimulatorError( + "All lineages died before stopping condition" + ) + + return cassiopeia_tree \ No newline at end of file diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 4f956ff0..2256bbe0 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -7,10 +7,11 @@ import networkx as nx import numpy as np +import pandas as pd from queue import PriorityQueue from cassiopeia.data.CassiopeiaTree import CassiopeiaTree -from cassiopeia.mixins import ecDNABirthDeathSimulatorError +from cassiopeia.mixins import ecDNABirthDeathSimulatorError, TreeSimulatorError from cassiopeia.simulator.BirthDeathFitnessSimulator import ( BirthDeathFitnessSimulator, ) @@ -391,27 +392,46 @@ def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: return new_ecdna_array - def populate_tree_from_simulation(self, tree: nx.DiGraph) -> CassiopeiaTree: + def populate_tree_from_simulation(self, tree: nx.DiGraph, observed_nodes: List[str]) -> CassiopeiaTree: """Populates tree with appropriate meta data. Args: tree: The tree simulated with ecDNA and fitness values populated as attributes. + observed_nodes: The observed leaves of the tree. Returns: - A CassiopeiaTree with node attributes filled in. + A CassiopeiaTree with relevant node attributes filled in. """ - cas_tree = CassiopeiaTree(tree=tree) + cassiopeia_tree = CassiopeiaTree(tree=tree) - for node in cas_tree.depth_first_traverse_nodes(): + time_dictionary = {} + for i in tree.nodes: + time_dictionary[i] = tree.nodes[i]["time"] + cassiopeia_tree.set_times(time_dictionary) + + # Prune dead lineages and collapse resulting unifurcations + to_remove = list(set(cassiopeia_tree.leaves) - set(observed_nodes)) + cassiopeia_tree.remove_leaves_and_prune_lineages(to_remove) + if self.collapse_unifurcations and len(cassiopeia_tree.nodes) > 1: + cassiopeia_tree.collapse_unifurcations(source="1") + + # If only implicit root remains after pruning dead lineages, error + if len(cassiopeia_tree.nodes) == 1: + raise ecDNABirthDeathSimulatorError( + "All lineages died before stopping condition" + ) + + for node in cassiopeia_tree.depth_first_traverse_nodes(): - cas_tree.set_attribute(node, 'ecdna_array', tree.nodes[node]['ecdna_array']) - cas_tree.set_attribute(node, 'fitness', tree.nodes[node]['birth_scale']) + cassiopeia_tree.set_attribute(node, 'ecdna_array', tree.nodes[node]['ecdna_array']) + cassiopeia_tree.set_attribute(node, 'fitness', tree.nodes[node]['birth_scale']) - cell_metadata = pd.DataFrame(columns = ['ecDNA{i}' for i in len(self.initial_copy_number)]) - for leaf in cas_tree.leaves: - cell_metadata.loc[leaf] = cas_tree.get_attribute(leaf, 'ecdna_array') + cell_metadata = pd.DataFrame(columns = [f'ecDNA_{i}' for i in range(len(self.initial_copy_number))]) + + for leaf in cassiopeia_tree.leaves: + cell_metadata.loc[leaf] = cassiopeia_tree.get_attribute(leaf, 'ecdna_array') - cas_tree.cell_meta = cell_metadata.copy() + cassiopeia_tree.cell_meta = cell_metadata.astype(int).copy() - return cas_tree \ No newline at end of file + return cassiopeia_tree \ No newline at end of file diff --git a/notebooks/benchmark.ipynb b/notebooks/benchmark.ipynb index d3b25b58..3f48958a 100755 --- a/notebooks/benchmark.ipynb +++ b/notebooks/benchmark.ipynb @@ -350,7 +350,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.13 ('ecass2')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -364,7 +364,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.9.12" }, "vscode": { "interpreter": { diff --git a/notebooks/ecDNA_sim_test.ipynb b/notebooks/ecDNA_sim_test.ipynb index 78f7c59c..3aaecede 100644 --- a/notebooks/ecDNA_sim_test.ipynb +++ b/notebooks/ecDNA_sim_test.ipynb @@ -527,7 +527,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.9.12" }, "vscode": { "interpreter": { diff --git a/test/simulator_tests/ecdna_birth_death_simulator_test.py b/test/simulator_tests/ecdna_birth_death_simulator_test.py index 318dac36..903a7482 100644 --- a/test/simulator_tests/ecdna_birth_death_simulator_test.py +++ b/test/simulator_tests/ecdna_birth_death_simulator_test.py @@ -1,15 +1,14 @@ """ Tests the functionality of cassiopeia.simulator.ecDNABirthDeathSimulator. """ - -from tkinter.tix import Tree +from typing import List, Tuple, Generator +from queue import PriorityQueue import unittest import networkx as nx import numpy as np +import pandas as pd -from typing import List, Tuple, Generator -from queue import PriorityQueue from cassiopeia.data.CassiopeiaTree import CassiopeiaTree @@ -21,6 +20,13 @@ import cassiopeia.data.utilities as utilities +def node_name_generator() -> Generator[str, None, None]: + """Generates unique node names for the tree.""" + i = 0 + while True: + yield str(i) + i += 1 + def extract_tree_statistics( tree: CassiopeiaTree, ) -> Tuple[List[float], int, bool]: @@ -52,6 +58,7 @@ def extract_tree_statistics( class ecDNABirthDeathSimulatorTest(unittest.TestCase): + def test_ecdna_splitting(self): np.random.seed(41) @@ -73,9 +80,9 @@ def test_ecdna_splitting(self): new_ecdna_array = sim.get_ecdna_array(0, tree) expected_array = [3, 3] # calculated manually as [4*2-5, 5*2 - 7] self.assertTrue(np.all(expected_array == new_ecdna_array)) + + def test_initial_sample_event(self): - def test_sample_lineage_event(self): - """Checks that sample_lineage_event behaves as expected under edge cases""" np.random.seed(41) sim = ecDNABirthDeathSimulator( birth_waiting_distribution=lambda _: 1, @@ -85,13 +92,6 @@ def test_sample_lineage_event(self): fitness_array = np.array([[[0,0.1], [0.1,0.2]],[[0.1,0.5],[0.2,0.6]]]), ) - def node_name_generator() -> Generator[str, None, None]: - """Generates unique node names for the tree.""" - i = 0 - while True: - yield str(i) - i += 1 - # first, we'll trigger the time=0 condition in sample_lineage_event. names = node_name_generator() @@ -122,15 +122,46 @@ def node_name_generator() -> Generator[str, None, None]: == tree.nodes[new_lineage["id"]]["ecdna_array"] ) ) - self.assertTrue(new_lineage["active"]) - self.assertTrue( - new_lineage["total_time"] > 0 - ) # does this really need to be true? yes + def test_basic_sample_lineage_events(self): + """Checks that sample_lineage_event behaves as expected under edge cases.""" + + np.random.seed(41) + sim = ecDNABirthDeathSimulator( + birth_waiting_distribution=lambda _: 1, + initial_birth_scale=1, + num_extant=16, + experiment_time=5, + fitness_array = np.array([[[0,0.1], [0.1,0.2]],[[0.1,0.5],[0.2,0.6]]]), + ) + + names = node_name_generator() + + tree = nx.DiGraph() + root = next(names) + tree.add_node(root) + tree.nodes[root]["birth_scale"] = 1 + tree.nodes[root]["time"] = 0 + tree.nodes[root]["ecdna_array"] = np.array([3, 2, 5]) + current_lineages = PriorityQueue() + observed_nodes = [] + starting_lineage = { + "id": root, + "birth_scale": tree.nodes[root]["birth_scale"], + "total_time": tree.nodes[root]["time"], + "active": True, + } + + sim.sample_lineage_event( + starting_lineage, current_lineages, tree, names, observed_nodes + ) + + _, _, new_lineage = current_lineages.get() # now, let's do one normal division sim.sample_lineage_event( new_lineage, current_lineages, tree, names, observed_nodes ) + # checked: the first ecdna_array is [2,0,6], so the other one should be [4,4,4] sim.sample_lineage_event( new_lineage, current_lineages, tree, names, observed_nodes @@ -161,6 +192,48 @@ def node_name_generator() -> Generator[str, None, None]: self.assertTrue(new_lineage["total_time"] == 5) self.assertTrue(not (new_lineage["active"])) + def test_populate_tree_from_simulation(self): + + np.random.seed(41) + sim = ecDNABirthDeathSimulator( + birth_waiting_distribution=lambda _: 1, + initial_birth_scale=1, + num_extant=16, + experiment_time=5, + initial_copy_number=[3,2,5] + ) + + names = node_name_generator() + + tree = nx.DiGraph() + root = next(names) + tree.add_node(root) + + child_1, child_2 = next(names), next(names) + tree.add_edges_from([(root, child_1), (root, child_2)]) + + tree.nodes[root]["birth_scale"] = 1 + tree.nodes[root]["time"] = 0 + tree.nodes[root]["ecdna_array"] = np.array([3, 2, 5]) + + tree.nodes[child_1]['birth_scale'] = 1 + tree.nodes[child_1]["time"] = 2 + tree.nodes[child_1]["ecdna_array"] = np.array([2,0,6]) + + tree.nodes[child_2]['birth_scale'] = 1 + tree.nodes[child_2]["time"] = 2 + tree.nodes[child_2]["ecdna_array"] = np.array([4, 4, 4]) + + cassiopeia_tree = sim.populate_tree_from_simulation(tree, [child_1, child_2]) + + expected_meta_data = pd.DataFrame.from_dict({ + child_1: [2, 0, 6], + child_2: [4, 4, 4] + }, orient='index', columns = ['ecDNA_0', 'ecDNA_1', 'ecDNA_2']) + + pd.testing.assert_frame_equal(expected_meta_data, cassiopeia_tree.cell_meta.astype(int)) + + if __name__ == "__main__": unittest.main() From 3f036854b839a250e2aa0c8e7abeb0aa4a2eb552 Mon Sep 17 00:00:00 2001 From: jpdsilva Date: Wed, 31 Aug 2022 21:05:33 -0700 Subject: [PATCH 15/37] adding metadata to tree and cleaning up ecDNA_sim_test --- .../simulator/BirthDeathFitnessSimulator.py | 13 +- .../simulator/ecDNABirthDeathSimulator.py | 6 + notebooks/ecDNA_sim_test.ipynb | 188 ++++-------------- 3 files changed, 54 insertions(+), 153 deletions(-) diff --git a/cassiopeia/simulator/BirthDeathFitnessSimulator.py b/cassiopeia/simulator/BirthDeathFitnessSimulator.py index c04965cb..307be79a 100644 --- a/cassiopeia/simulator/BirthDeathFitnessSimulator.py +++ b/cassiopeia/simulator/BirthDeathFitnessSimulator.py @@ -273,7 +273,7 @@ def node_name_generator() -> Generator[str, None, None]: lineage, current_lineages, tree, names, observed_nodes ) - cassiopeia_tree = CassiopeiaTree(tree=tree) + cassiopeia_tree = self.populate_tree_from_simulation(tree=tree) time_dictionary = {} for i in tree.nodes: time_dictionary[i] = tree.nodes[i]["time"] @@ -493,4 +493,13 @@ def make_lineage_dict( "total_time": total_time, "active": active_flag, } - return lineage \ No newline at end of file + return lineage + + def populate_tree_from_simulation(self, tree: nx.DiGraph) -> CassiopeiaTree: + """Populates tree with appropriate meta data""" + + cas_tree = CassiopeiaTree(tree=tree) + + # add ecDNA (and optionally fitness of nodes) to tree as attributes + # make sure you add ecDNA as a pandas dataframe and pass it as cell_meta. + return cas_tree \ No newline at end of file diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 740d7a52..d2c85fdc 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -9,6 +9,7 @@ import numpy as np from queue import PriorityQueue +from cassiopeia.data.CassiopeiaTree import CassiopeiaTree from cassiopeia.mixins import ecDNABirthDeathSimulatorError from cassiopeia.simulator.BirthDeathFitnessSimulator import ( BirthDeathFitnessSimulator, @@ -394,5 +395,10 @@ def populate_tree_from_simulation(self, tree: nx.DiGraph) -> CassiopeiaTree: cas_tree = CassiopeiaTree(tree=tree) # add ecDNA (and optionally fitness of nodes) to tree as attributes + for node in tree.nodes: + cas_tree.set_attribute(node, attribute_name = "fitness", value=((tree.nodes[node]["birth_scale"] / self.initial_birth_scale) - 1) ) + cas_tree.set_attribute(node, attribute_name = "ecdna_array", value=tree.nodes[node]["ecdna_array"]) + # make sure you add ecDNA as a pandas dataframe and pass it as cell_meta. + return cas_tree \ No newline at end of file diff --git a/notebooks/ecDNA_sim_test.ipynb b/notebooks/ecDNA_sim_test.ipynb index 78f7c59c..827e9340 100644 --- a/notebooks/ecDNA_sim_test.ipynb +++ b/notebooks/ecDNA_sim_test.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Cassiopeia Benchmarking Pipeline\n", + "# Cassiopeia ecDNA simulations\n", "\n", - "Here, we simulate trees with ecDNA. Based on benchmark.ipynb \n", + "Here, we simulate a population of cells with extrachromosomal DNA (ecDNA), circular, acentromeric DNA molecules that arise in cancers and segregate randomly at mitosis. This tutorial is based on benchmark.ipynb and we quote / borrow extensively from it. \n", "\n", "\"You can install Cassiopeia by following the guide [here](https://cassiopeia-lineage.readthedocs.io/en/latest/installation.html).\"\n", "\n", @@ -17,7 +17,21 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "positional argument follows keyword argument (ecDNABirthDeathSimulator.py, line 401)", + "output_type": "error", + "traceback": [ + "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n", + " File \u001b[1;32m~/miniconda3/envs/ecass2/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3398\u001b[0m in \u001b[1;35mrun_code\u001b[0m\n exec(code_obj, self.user_global_ns, self.user_ns)\n", + " Input \u001b[1;32mIn [1]\u001b[0m in \u001b[1;35m\u001b[0m\n import cassiopeia as cas\n", + " File \u001b[1;32m~/miniconda3/envs/ecass2/lib/python3.9/site-packages/cassiopeia/__init__.py:10\u001b[0m in \u001b[1;35m\u001b[0m\n from . import simulator as sim\n", + "\u001b[0;36m File \u001b[0;32m~/miniconda3/envs/ecass2/lib/python3.9/site-packages/cassiopeia/simulator/__init__.py:8\u001b[0;36m in \u001b[0;35m\u001b[0;36m\u001b[0m\n\u001b[0;31m from .ecDNABirthDeathSimulator import ecDNABirthDeathSimulator\u001b[0m\n", + "\u001b[0;36m File \u001b[0;32m~/miniconda3/envs/ecass2/lib/python3.9/site-packages/cassiopeia/simulator/ecDNABirthDeathSimulator.py:401\u001b[0;36m\u001b[0m\n\u001b[0;31m cas_tree.set_attribute(node, attribute_name = \"ecdna_array\", tree.nodes[node][\"ecdna_array\"])\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n" + ] + } + ], "source": [ "import cProfile\n", "from collections import defaultdict\n", @@ -35,52 +49,25 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.display import Image\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's where I recall python syntax / test code snippets" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "inf" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.inf" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tree Simulation\n", "\n", - "ecDNABirthDeathSimulator is based on BirthDeathFitnesSimulator, which is documented [here](https://cassiopeia-lineage.readthedocs.io/en/latest/api/reference/cassiopeia.sim.BirthDeathFitnessSimulator.html). " + "ecDNABirthDeathSimulator is a subclass of BirthDeathFitnesSimulator, which is documented [here](https://cassiopeia-lineage.readthedocs.io/en/latest/api/reference/cassiopeia.sim.BirthDeathFitnessSimulator.html). " ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -104,142 +91,41 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's stare manually at a few ecDNA CNs. One weird thing I'm noticing is that there isn't conservation of copy number right now. This could be b/c of death. so I'll re-run with infinite time to death.\n", - "I tried this and it still doesn't conserve CN, which is weird. \n", - "\n", - "I figured out why it doesn't conserve CN. It's because there's a weird passage of cell 0 to cell 1 before division is allowed to start occurring. Let's figure out where/why this happens and stop it. \n", - "\n", - "It happens in simulate_tree (inherited from BirthDeathSimulator), because in order to sample the first waiting time, we call sample_lineage_event. I fixed this in a really hack-y way (see ecDNABirthDeathSimulator.py, search for \"TO DO\" to see what I mean). No wait, this fix doesn't work; it seems liek the bypass is *always* being triggered. Let's stare at that and try to see why.\n", - "\n", - "I didn't figure out why the other condition in sample_lineage_event didn't work, but this one (checking whether we're at time 0) does. So i'll go with it for now.\n", - "\n", - "I manually checked that the ecDNA CN are doubling every generation (for two generations).\n" + "We can manually inspect the tree (when the number of cells is not too large) to see how random segregation leads to asymmetric CN. " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'graph': {},\n", - " '_node': {'0': {'birth_scale': 0.5,\n", - " 'time': 0,\n", - " 'ecdna_array': array([7, 3, 5]),\n", - " 'character_states': []},\n", - " '1': {'birth_scale': 0.8,\n", - " 'time': 0.17454120776779053,\n", - " 'ecdna_array': array([7, 3, 5]),\n", - " 'character_states': []},\n", - " '2': {'birth_scale': 0.8,\n", - " 'time': 0.7795586320719254,\n", - " 'ecdna_array': array([5, 1, 6]),\n", - " 'character_states': []},\n", - " '3': {'birth_scale': 0.8,\n", - " 'time': 1.0290081133380253,\n", - " 'ecdna_array': array([9, 5, 4]),\n", - " 'character_states': []},\n", - " '4': {'birth_scale': 0.8,\n", - " 'time': 1.0785464630165777,\n", - " 'ecdna_array': array([5, 0, 5]),\n", - " 'character_states': []},\n", - " '5': {'birth_scale': 0.8,\n", - " 'time': 1.0785464630165777,\n", - " 'ecdna_array': array([5, 2, 7]),\n", - " 'character_states': []},\n", - " '6': {'birth_scale': 0.8,\n", - " 'time': 1.0785464630165777,\n", - " 'ecdna_array': array([11, 7, 2]),\n", - " 'character_states': []},\n", - " '7': {'birth_scale': 0.8,\n", - " 'time': 1.0785464630165777,\n", - " 'ecdna_array': array([7, 3, 6]),\n", - " 'character_states': []}},\n", - " '_adj': {'0': {'1': {'length': 0.17454120776779053}},\n", - " '1': {'2': {'length': 0.6050174243041349},\n", - " '3': {'length': 0.8544669055702347}},\n", - " '2': {'4': {'length': 0.2989878309446523},\n", - " '5': {'length': 0.2989878309446523}},\n", - " '3': {'6': {'length': 0.04953834967855242},\n", - " '7': {'length': 0.04953834967855242}},\n", - " '4': {},\n", - " '5': {},\n", - " '6': {},\n", - " '7': {}},\n", - " '_succ': {'0': {'1': {'length': 0.17454120776779053}},\n", - " '1': {'2': {'length': 0.6050174243041349},\n", - " '3': {'length': 0.8544669055702347}},\n", - " '2': {'4': {'length': 0.2989878309446523},\n", - " '5': {'length': 0.2989878309446523}},\n", - " '3': {'6': {'length': 0.04953834967855242},\n", - " '7': {'length': 0.04953834967855242}},\n", - " '4': {},\n", - " '5': {},\n", - " '6': {},\n", - " '7': {}},\n", - " '_pred': {'0': {},\n", - " '1': {'0': {'length': 0.17454120776779053}},\n", - " '2': {'1': {'length': 0.6050174243041349}},\n", - " '3': {'1': {'length': 0.8544669055702347}},\n", - " '4': {'2': {'length': 0.2989878309446523}},\n", - " '5': {'2': {'length': 0.2989878309446523}},\n", - " '6': {'3': {'length': 0.04953834967855242}},\n", - " '7': {'3': {'length': 0.04953834967855242}}},\n", - " 'nodes': NodeView(('0', '1', '2', '3', '4', '5', '6', '7')),\n", - " 'succ': AdjacencyView({'0': {'1': {'length': 0.17454120776779053}}, '1': {'2': {'length': 0.6050174243041349}, '3': {'length': 0.8544669055702347}}, '2': {'4': {'length': 0.2989878309446523}, '5': {'length': 0.2989878309446523}}, '3': {'6': {'length': 0.04953834967855242}, '7': {'length': 0.04953834967855242}}, '4': {}, '5': {}, '6': {}, '7': {}}),\n", - " 'edges': OutEdgeView([('0', '1'), ('1', '2'), ('1', '3'), ('2', '4'), ('2', '5'), ('3', '6'), ('3', '7')]),\n", - " 'adj': AdjacencyView({'0': {'1': {'length': 0.17454120776779053}}, '1': {'2': {'length': 0.6050174243041349}, '3': {'length': 0.8544669055702347}}, '2': {'4': {'length': 0.2989878309446523}, '5': {'length': 0.2989878309446523}}, '3': {'6': {'length': 0.04953834967855242}, '7': {'length': 0.04953834967855242}}, '4': {}, '5': {}, '6': {}, '7': {}}),\n", - " 'in_degree': InDegreeView({'0': 0, '1': 1, '2': 1, '3': 1, '4': 1, '5': 1, '6': 1, '7': 1}),\n", - " 'out_degree': OutDegreeView({'0': 1, '1': 2, '2': 2, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0})}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# ground_truth_tree.__dict__ \n", "ground_truth_tree._CassiopeiaTree__network.__dict__" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for node in ground_truth_tree.get_tree_topology().nodes:\n", + " print(node)" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We canNOT upload and export trees to iTOL (iTOL is no longer free). Instead, we can use the local visualization library. Learn more in local_plotting.ipynb. " + "Unfortunately, iTOL is no longer free. However, there is local plotting functionality. For tutorials, see Cassiopeia/notebooks/local_plotting.ipynb" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
, )" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cas.pl.plot_matplotlib(ground_truth_tree, add_root=True)" ] @@ -248,7 +134,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, let's try to annotate the tree with the copy numbers." + "Now, let's annotate the tree with the copy numbers." ] }, { From d297d0997bf95c8e94100c961eaa9297069fe67f Mon Sep 17 00:00:00 2001 From: jpdsilva Date: Wed, 31 Aug 2022 21:07:11 -0700 Subject: [PATCH 16/37] edits to notebook --- notebooks/benchmark.ipynb | 275 +- notebooks/local_plotting.ipynb | 202151 +----------------------------- 2 files changed, 270 insertions(+), 202156 deletions(-) diff --git a/notebooks/benchmark.ipynb b/notebooks/benchmark.ipynb index d3b25b58..ec2d08a8 100755 --- a/notebooks/benchmark.ipynb +++ b/notebooks/benchmark.ipynb @@ -76,7 +76,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We canNOT upload and export trees to iTOL which we can visualize in line. You can toggle branch lengths with the `use_branch_lengths` parameter. (iTOL is no longer free). Instead, we can use the local visualization library." + "Unfortunately, iTOL is no longer free. However, there is local plotting functionality. For tutorials, see Cassiopeia/notebooks/local_plotting.ipynb" ] }, { @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -151,9 +151,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We simulated 40 characters across 400 cells.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "## inspect character matrix\n", "\n", @@ -189,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -201,9 +233,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "cas.pl.plot_matplotlib(reconstructed_tree, add_root=True)" ] @@ -219,9 +274,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized RF: 0.6891891891891891\n" + ] + } + ], "source": [ "reconstructed_tree.collapse_mutationless_edges(infer_ancestral_characters = True)\n", "\n", @@ -232,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -259,9 +322,164 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "91c16bf5b0b14fb3b38ea636f2831ec9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/10 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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L37.GAGGTGACATCGATGT-1", - "BRANCH
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L37.GAGGTGACATCGATGT-1", - "BRANCH
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L37.TACGGATGTCGCCATG-1", - "BRANCH
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L37.CTAGAGTAGCCCTAAT-1", - "BRANCH
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L37.AAGGAGCTCTACCTGC-1", - "BRANCH
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L37.ACATCAGGTCGGATCC-1", - "BRANCH
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L37.ACATCAGGTCGGATCC-1", - "BRANCH
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L37.CGAGCACTCAAGGTAA-1", - "BRANCH
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L37.GCTCTGTTCGAGAGCA-1", - "BRANCH
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L37.TGAGGGACATAGAAAC-1", - "BRANCH
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L37.TGAGGGACATAGAAAC-1", - "BRANCH
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L37.GACACGCCAATAAGCA-1", - "BRANCH
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L37.GACACGCCAATAAGCA-1", - "BRANCH
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L37.CAGTAACAGCGATATA-1", - "BRANCH
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L37.AATCGGTTCAGGCAAG-1", - "BRANCH
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L37.AATCGGTTCAGGCAAG-1", - "BRANCH
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L37.AGATTGCTCTCGTTTA-1", - "BRANCH
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L37.AGATTGCTCTCGTTTA-1", - "BRANCH
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L37.CATGACACATCATCCC-1", - "BRANCH
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L37.CATGACACATCATCCC-1", - "BRANCH
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L37.AGCCTAATCTCAAACG-1", - "BRANCH
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L37.AGCCTAATCTCAAACG-1", - "BRANCH
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L37.GTTCATTTCCAGGGCT-1", - "BRANCH
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L37.GTTCATTTCCAGGGCT-1", - "BRANCH
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L37.ACGGAGACAAGGGTCA-1", - "BRANCH
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L37.GCGCCAAAGCCTTGAT-1", - "BRANCH
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L37.GCGCCAAAGCCTTGAT-1", - "BRANCH
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L37.CAGAATCGTTATCGGT-1", - "BRANCH
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L37.CAGAATCGTTATCGGT-1", - "BRANCH
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L37.CGTTCTGCATTCCTCG-1", - "BRANCH
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L37.CGTTCTGCATTCCTCG-1", - "BRANCH
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L37.GCCTCTAGTGCTGTAT-1", - "BRANCH
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L37.TCTCTAAGTTTGGGCC-1", - "BRANCH
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L37.TCTCTAAGTTTGGGCC-1", - "BRANCH
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L37.GACGCGTGTGCAGTAG-1", - "BRANCH
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L37.GACGCGTGTGCAGTAG-1", - "BRANCH
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L37.CATCGAAGTTTGTTTC-1", - "BRANCH
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L37.CATCGAAGTTTGTTTC-1", - "BRANCH
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L37.GAACGGAAGGCAGGTT-1", - "BRANCH
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L37.TTGACTTAGCTAAGAT-1", - "BRANCH
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L37.TTGACTTAGCTAAGAT-1", - "BRANCH
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L37.CAGCTAATCAGGCCCA-1", - "BRANCH
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L37.CAGCTAATCAGGCCCA-1", - "BRANCH
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L37.TATCAGGTCACGATGT-1", - "BRANCH
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2|2|2|-|-|-|-|-|-|2|2|7|-|-|2|2|0|2|2|4|7|3|0|3|3|4|4|3|16", - "BRANCH
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2|2|2|-|-|-|-|-|-|2|2|7|-|-|2|2|0|2|2|4|7|3|0|3|3|4|4|3|16", - "BRANCH
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2|2|2|-|-|-|-|-|-|2|2|7|-|-|2|2|0|2|2|4|7|3|3|3|3|4|4|3|16", - "BRANCH
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2|2|2|-|-|-|-|-|-|2|2|7|-|-|2|2|0|2|2|4|7|3|3|3|3|4|4|3|16", - "BRANCH
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2|2|2|-|-|-|-|-|-|2|2|7|-|-|2|2|0|2|2|4|7|3|3|3|3|4|4|3|16
L37.GGTGAAGAGCAACGGT-1", - "BRANCH
2|2|2|-|-|-|-|-|-|2|2|7|-|-|2|2|0|2|2|4|7|3|3|3|3|4|4|3|16
L37.GGTGAAGAGCAACGGT-1", - "BRANCH
2|2|2|-|-|-|-|-|-|2|2|7|-|-|2|2|0|2|2|4|7|3|3|3|3|4|4|3|16
L37.GGTGAAGAGCAACGGT-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 105, - 105, - 105 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|4|2|10|-|-|-|2|2|7|-|-|2|2|9|2|2|4|7|3|0|3|3|0|4|3|16", - "BRANCH
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2|2|2|4|2|10|-|-|-|2|2|7|-|-|2|2|9|2|2|4|7|3|0|3|3|4|4|3|16", - "BRANCH
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2|2|2|4|2|10|-|-|-|2|2|7|-|-|2|2|9|2|2|4|7|3|0|3|3|4|4|3|16", - "BRANCH
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2|2|2|4|2|10|-|-|-|2|2|7|-|-|2|2|9|2|2|4|7|3|0|3|3|4|4|3|16
L37.GGATGTTAGAACTGTA-1", - "BRANCH
2|2|2|4|2|10|-|-|-|2|2|7|-|-|2|2|9|2|2|4|7|3|0|3|3|4|4|3|16
L37.GGATGTTAGAACTGTA-1", - "BRANCH
2|2|2|4|2|10|-|-|-|2|2|7|-|-|2|2|9|2|2|4|7|3|0|3|3|4|4|3|16
L37.GGATGTTAGAACTGTA-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 106, - 106, - 106 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|-|-|-|-|-|-|2|2|7|-|-|2|2|9|2|2|4|7|3|2|3|3|0|4|3|16", - "BRANCH
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2|2|2|-|-|-|-|-|-|2|2|7|-|-|2|2|9|2|2|4|7|3|2|3|3|0|4|3|16", - "BRANCH
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2|2|2|-|-|-|-|-|-|2|2|7|-|-|-|-|-|2|2|4|7|3|2|-|-|-|4|3|16", - "BRANCH
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2|2|2|-|-|-|-|-|-|2|2|7|-|-|-|-|-|2|2|4|7|3|2|-|-|-|4|3|16
L37.GTTACAGGTGTTTGTG-1", - "BRANCH
2|2|2|-|-|-|-|-|-|2|2|7|-|-|-|-|-|2|2|4|7|3|2|-|-|-|4|3|16
L37.GTTACAGGTGTTTGTG-1", - "BRANCH
2|2|2|-|-|-|-|-|-|2|2|7|-|-|-|-|-|2|2|4|7|3|2|-|-|-|4|3|16
L37.GTTACAGGTGTTTGTG-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 107, - 107, - 107 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|-|-|-|-|-|-|2|2|7|-|-|2|2|9|2|2|4|7|3|2|3|3|0|4|3|16
L37.AGATTGCGTACTTCTT-1", - "BRANCH
2|2|2|-|-|-|-|-|-|2|2|7|-|-|2|2|9|2|2|4|7|3|2|3|3|0|4|3|16
L37.AGATTGCGTACTTCTT-1", - "BRANCH
2|2|2|-|-|-|-|-|-|2|2|7|-|-|2|2|9|2|2|4|7|3|2|3|3|0|4|3|16
L37.AGATTGCGTACTTCTT-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 107.5, - 108, - 108 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|4|2|10|-|-|-|2|2|12|0|10|2|2|0|2|2|4|3|3|0|3|3|0|7|3|0", - "BRANCH
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2|2|2|4|2|10|-|-|-|2|2|12|0|10|2|2|0|2|2|4|3|3|0|3|3|0|7|3|0" - ], - "type": "scatter", - "x": [ - -3, - -3, - -2 - ], - "y": [ - 109.5, - 109, - 109 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|4|2|10|-|-|-|2|2|12|0|10|2|2|0|2|2|4|3|3|0|3|3|0|7|3|0
2|2|2|4|2|10|-|-|-|2|2|12|4|10|2|2|0|2|2|4|3|3|11|3|3|0|7|3|0", - "BRANCH
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2|2|2|4|2|10|-|-|-|2|2|12|4|10|2|2|0|2|2|4|3|3|11|3|3|0|7|3|0", - "BRANCH
2|2|2|4|2|10|-|-|-|2|2|12|0|10|2|2|0|2|2|4|3|3|0|3|3|0|7|3|0
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2|2|2|4|2|10|-|-|-|2|2|12|4|10|2|2|0|2|2|4|3|3|11|3|3|0|7|3|0
L37.CTACATTGTTCGCGAC-1", - "BRANCH
2|2|2|4|2|10|-|-|-|2|2|12|4|10|2|2|0|2|2|4|3|3|11|3|3|0|7|3|0
L37.CTACATTGTTCGCGAC-1", - "BRANCH
2|2|2|4|2|10|-|-|-|2|2|12|4|10|2|2|0|2|2|4|3|3|11|3|3|0|7|3|0
L37.CTACATTGTTCGCGAC-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 109, - 109, - 109 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|4|2|10|-|-|-|2|2|12|0|0|2|2|0|2|2|4|3|3|0|3|3|0|7|3|4
2|2|2|4|2|10|-|-|-|2|2|12|10|5|2|2|0|2|2|4|3|3|0|3|3|0|7|3|4", - "BRANCH
2|2|2|4|2|10|-|-|-|2|2|12|0|0|2|2|0|2|2|4|3|3|0|3|3|0|7|3|4
2|2|2|4|2|10|-|-|-|2|2|12|10|5|2|2|0|2|2|4|3|3|0|3|3|0|7|3|4", - "BRANCH
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2|2|2|4|2|10|-|-|-|2|2|12|10|5|2|2|0|2|2|4|3|3|0|3|3|0|7|3|4
L37.AGTGAGGTCGGTCTAA-1", - "BRANCH
2|2|2|4|2|10|-|-|-|2|2|12|10|5|2|2|0|2|2|4|3|3|0|3|3|0|7|3|4
L37.AGTGAGGTCGGTCTAA-1", - "BRANCH
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L37.GATGAGGAGGAGTTGC-1", - "BRANCH
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L37.GATGAGGAGGAGTTGC-1", - "BRANCH
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L37.AGATTGCAGGCAATTA-1", - "BRANCH
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L37.ATTGGTGAGGCCGAAT-1", - "BRANCH
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L37.TCAGCAACACCATGTA-1", - "BRANCH
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L37.CAGAATCCAAGGCTCC-1", - "BRANCH
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2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|8|6|7" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 125.5, - 125.5, - 125.5 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|8|6|7
L37.TACTCATTCGGTTCGG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|8|6|7
L37.TACTCATTCGGTTCGG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|8|6|7
L37.TACTCATTCGGTTCGG-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 125.5, - 125, - 125 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|8|6|7
L37.CCATTCGAGCGATGAC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|8|6|7
L37.CCATTCGAGCGATGAC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|8|6|7
L37.CCATTCGAGCGATGAC-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 125.5, - 126, - 126 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|3|3|0|0|6|0
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|3|3|0|0|6|16", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|3|3|0|0|6|0
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|3|3|0|0|6|16", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|3|3|0|0|6|0
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|3|3|0|0|6|16" - ], - "type": "scatter", - "x": [ - -6, - -6, - -4 - ], - "y": [ - 135.34375, - 136.5, - 136.5 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|3|3|0|0|6|16
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|3|3|0|0|6|16
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|3|3|0|0|6|16
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-" - ], - "type": "scatter", - "x": [ - -4, - -4, - -3 - ], - "y": [ - 136.5, - 133, - 133 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-
-|-|-|-|-|-|2|2|0|2|2|2|-|-|2|2|0|-|-|-|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-
-|-|-|-|-|-|2|2|0|2|2|2|-|-|2|2|0|-|-|-|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-
-|-|-|-|-|-|2|2|0|2|2|2|-|-|2|2|0|-|-|-|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -3, - -3, - -1 - ], - "y": [ - 133, - 127, - 127 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
-|-|-|-|-|-|2|2|0|2|2|2|-|-|2|2|0|-|-|-|-|-|-|-|-|-|-|-|-
L37.ACATGGTAGGACCACA-1", - "BRANCH
-|-|-|-|-|-|2|2|0|2|2|2|-|-|2|2|0|-|-|-|-|-|-|-|-|-|-|-|-
L37.ACATGGTAGGACCACA-1", - "BRANCH
-|-|-|-|-|-|2|2|0|2|2|2|-|-|2|2|0|-|-|-|-|-|-|-|-|-|-|-|-
L37.ACATGGTAGGACCACA-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 127, - 127, - 127 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -3, - -3, - -2 - ], - "y": [ - 133, - 133, - 133 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 133, - 128, - 128 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGCAATTTCACCTCGT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGCAATTTCACCTCGT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGCAATTTCACCTCGT-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 128, - 128, - 128 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|-|-|-|2|2|0|-|-|-|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|-|-|-|2|2|0|-|-|-|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|-|-|-|2|2|0|-|-|-|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 133, - 129, - 129 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|-|-|-|2|2|0|-|-|-|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TGCACCTAGTACACCT-1", - "BRANCH
2|2|2|-|-|-|2|2|0|-|-|-|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TGCACCTAGTACACCT-1", - "BRANCH
2|2|2|-|-|-|2|2|0|-|-|-|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TGCACCTAGTACACCT-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 129, - 129, - 129 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|-|-|-|2|2|0|2|2|2|-|-|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|-|-|-|2|2|0|2|2|2|-|-|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|-|-|-|2|2|0|2|2|2|-|-|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 133, - 130, - 130 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|-|-|-|2|2|0|2|2|2|-|-|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-
L37.AGAGCTTTCAAGATCC-1", - "BRANCH
2|2|2|-|-|-|2|2|0|2|2|2|-|-|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-
L37.AGAGCTTTCAAGATCC-1", - "BRANCH
2|2|2|-|-|-|2|2|0|2|2|2|-|-|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-
L37.AGAGCTTTCAAGATCC-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 130, - 130, - 130 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|13|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|13|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|13|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 133, - 131, - 131 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|13|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CTGAAACCATTTGCCC-1", - "BRANCH
2|2|2|2|2|13|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CTGAAACCATTTGCCC-1", - "BRANCH
2|2|2|2|2|13|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CTGAAACCATTTGCCC-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 131, - 131, - 131 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TCTATTGGTGAGTATA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TCTATTGGTGAGTATA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TCTATTGGTGAGTATA-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 133, - 132, - 132 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TCCCGATCACCGAATT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TCCCGATCACCGAATT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TCCCGATCACCGAATT-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 133, - 133, - 133 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGGAATGAGCCACCTG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGGAATGAGCCACCTG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGGAATGAGCCACCTG-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 133, - 134, - 134 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGACATTAGGAGTCTG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGACATTAGGAGTCTG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGACATTAGGAGTCTG-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 133, - 135, - 135 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GCTCTGTGTATGAAAC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GCTCTGTGTATGAAAC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GCTCTGTGTATGAAAC-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 133, - 136, - 136 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CTTAGGAGTGCACTTA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CTTAGGAGTGCACTTA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CTTAGGAGTGCACTTA-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 133, - 137, - 137 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.AGATCTGAGGCTACGA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.AGATCTGAGGCTACGA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.AGATCTGAGGCTACGA-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 133, - 138, - 138 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-
2|2|2|2|2|0|2|2|3|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-
2|2|2|2|2|0|2|2|3|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-
2|2|2|2|2|0|2|2|3|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-" - ], - "type": "scatter", - "x": [ - -3, - -3, - -1 - ], - "y": [ - 133, - 139, - 139 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|3|2|2|2|-|-|2|2|0|2|2|2|5|6|3|3|3|0|-|-|-
L37.GTTCTCGAGTCGATAA-1", - "BRANCH
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L37.GTTCTCGAGTCGATAA-1", - "BRANCH
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L37.GCTCCTAAGCTGCGAA-1", - "BRANCH
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L37.GCTCCTAAGCTGCGAA-1", - "BRANCH
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L37.TCACAAGAGTGAACGC-1", - "BRANCH
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L37.TCACAAGAGTGAACGC-1", - "BRANCH
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L37.AGATTGCAGAGGACGG-1", - "BRANCH
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L37.AGATTGCAGAGGACGG-1", - "BRANCH
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L37.AAAGATGGTAGCGTGA-1", - "BRANCH
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L37.AAAGATGGTAGCGTGA-1", - "BRANCH
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L37.ATCTACTAGCTCTCGG-1", - "BRANCH
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L37.ATCTACTAGCTCTCGG-1", - "BRANCH
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L37.CATATTCGTCGAATCT-1", - "BRANCH
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L37.CATATTCGTCGAATCT-1", - "BRANCH
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L37.CATATTCGTCGAATCT-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 146.5, - 146, - 146 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.ATGGGAGCAAAGGAAG-1", - "BRANCH
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L37.ATGGGAGCAAAGGAAG-1", - "BRANCH
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L37.ATGGGAGCAAAGGAAG-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 146.5, - 147, - 147 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.GGGAGATGTCAATACC-1", - "BRANCH
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L37.GGGAGATGTCAATACC-1", - "BRANCH
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L37.GGGAGATGTCAATACC-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 148, - 148, - 148 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.CGTCACTAGTCCAGGA-1", - "BRANCH
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L37.CGTCACTAGTCCAGGA-1", - "BRANCH
2|2|2|2|2|0|2|2|9|2|2|2|2|0|2|2|0|2|2|2|-|-|-|3|3|0|-|-|-
L37.CGTCACTAGTCCAGGA-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 148.5, - 149, - 149 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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-|-|-|-|-|-|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGATGTTTCCGGCACA-1", - "BRANCH
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L37.GGATGTTTCCGGCACA-1", - "BRANCH
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L37.GGATGTTTCCGGCACA-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 150, - 150, - 150 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|-|-|-|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGTGAAGGTATGAATG-1", - "BRANCH
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L37.GGTGAAGGTATGAATG-1", - "BRANCH
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L37.GGTGAAGGTATGAATG-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 150.5, - 151, - 151 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.CGTTGGGGTTCACGGC-1", - "BRANCH
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L37.CGTTGGGGTTCACGGC-1", - "BRANCH
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L37.CGTTGGGGTTCACGGC-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 152, - 152, - 152 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-
L37.CTTAACTAGACTAGAT-1", - "BRANCH
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L37.CTTAACTAGACTAGAT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-
L37.CTTAACTAGACTAGAT-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 153, - 153, - 153 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -4, - -4, - -2 - ], - "y": [ - 174, - 170.25, - 170.25 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|0|-|-|-|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|0|-|-|-|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|0|-|-|-|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 170.25, - 154.5, - 154.5 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|-|-|-|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GCTTGAAGTTCAACCA-1", - "BRANCH
2|2|2|2|2|0|-|-|-|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GCTTGAAGTTCAACCA-1", - "BRANCH
2|2|2|2|2|0|-|-|-|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GCTTGAAGTTCAACCA-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 154.5, - 154, - 154 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|-|-|-|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GCAAACTTCACGCATA-1", - "BRANCH
2|2|2|2|2|0|-|-|-|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GCAAACTTCACGCATA-1", - "BRANCH
2|2|2|2|2|0|-|-|-|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GCAAACTTCACGCATA-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 154.5, - 155, - 155 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|0|2|2|6|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|0|2|2|6|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|0|2|2|6|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 170.25, - 156, - 156 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|6|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TGAGCCGCATGCAATC-1", - "BRANCH
2|2|2|2|2|0|2|2|6|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TGAGCCGCATGCAATC-1", - "BRANCH
2|2|2|2|2|0|2|2|6|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TGAGCCGCATGCAATC-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 156, - 156, - 156 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|20|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|20|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
2|2|2|2|2|20|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 170.25, - 157, - 157 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|20|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.ATGCGATAGTAGGTGC-1", - "BRANCH
2|2|2|2|2|20|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.ATGCGATAGTAGGTGC-1", - "BRANCH
2|2|2|2|2|20|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.ATGCGATAGTAGGTGC-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 157, - 157, - 157 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TTTGCGCCAAACTGCT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TTTGCGCCAAACTGCT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TTTGCGCCAAACTGCT-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 158, - 158 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TTCTCAATCCTTGCCA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TTCTCAATCCTTGCCA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TTCTCAATCCTTGCCA-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 159, - 159 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TGGTTCCGTAAGTGTA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TGGTTCCGTAAGTGTA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TGGTTCCGTAAGTGTA-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 160, - 160 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TGAGCATCAAGCGAGT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TGAGCATCAAGCGAGT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TGAGCATCAAGCGAGT-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 161, - 161 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TCTTCGGTCTTACCTA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TCTTCGGTCTTACCTA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TCTTCGGTCTTACCTA-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 162, - 162 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TCATTACAGCTTTGGT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TCATTACAGCTTTGGT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TCATTACAGCTTTGGT-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 163, - 163 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TATTACCGTCTCTTAT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TATTACCGTCTCTTAT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TATTACCGTCTCTTAT-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 164, - 164 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TACTTGTTCCAAATGC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TACTTGTTCCAAATGC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TACTTGTTCCAAATGC-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 165, - 165 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGTGAAGCATATGCTG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGTGAAGCATATGCTG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGTGAAGCATATGCTG-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 166, - 166 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGTATTGCAGCCTTTC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGTATTGCAGCCTTTC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GGTATTGCAGCCTTTC-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 167, - 167 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GCTTCCATCGTAGATC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GCTTCCATCGTAGATC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GCTTCCATCGTAGATC-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 168, - 168 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GATGAAACAAGGTTCT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GATGAAACAAGGTTCT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GATGAAACAAGGTTCT-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 169, - 169 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CGTGTAAAGGCTAGGT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CGTGTAAAGGCTAGGT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CGTGTAAAGGCTAGGT-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 170, - 170 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CGTAGGCCATTCCTGC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CGTAGGCCATTCCTGC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CGTAGGCCATTCCTGC-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 171, - 171 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CGCGTTTGTCGCGAAA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CGCGTTTGTCGCGAAA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CGCGTTTGTCGCGAAA-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 172, - 172 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CGCGGTAAGCCACGCT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CGCGGTAAGCCACGCT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CGCGGTAAGCCACGCT-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 173, - 173 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CCTATTAAGTGAATTG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CCTATTAAGTGAATTG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CCTATTAAGTGAATTG-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 174, - 174 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CCGTTCAAGGACGAAA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CCGTTCAAGGACGAAA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CCGTTCAAGGACGAAA-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 175, - 175 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CATGACAAGGAGTTGC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CATGACAAGGAGTTGC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CATGACAAGGAGTTGC-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 176, - 176 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CACCACTAGCCAGAAC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CACCACTAGCCAGAAC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CACCACTAGCCAGAAC-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 177, - 177 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CACAGGCGTTCACCTC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CACAGGCGTTCACCTC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CACAGGCGTTCACCTC-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 178, - 178 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CACACCTCACGACTCG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CACACCTCACGACTCG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CACACCTCACGACTCG-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 179, - 179 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CACAAACTCACGATGT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CACAAACTCACGATGT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CACAAACTCACGATGT-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 180, - 180 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CAACCTCAGGACGAAA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CAACCTCAGGACGAAA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CAACCTCAGGACGAAA-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 181, - 181 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.AGGGAGTAGACCCACC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.AGGGAGTAGACCCACC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.AGGGAGTAGACCCACC-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 182, - 182 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.AGAATAGGTCGCTTCT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.AGAATAGGTCGCTTCT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.AGAATAGGTCGCTTCT-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 183, - 183 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.ACTGATGTCTACTTAC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.ACTGATGTCTACTTAC-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.ACTGATGTCTACTTAC-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 184, - 184 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.ACTATCTAGGTGACCA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.ACTATCTAGGTGACCA-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.ACTATCTAGGTGACCA-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 185, - 185 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.ACAGCTACAGCTGGCT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.ACAGCTACAGCTGGCT-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.ACAGCTACAGCTGGCT-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 170.25, - 186, - 186 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|4|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|4|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|4|2|2|2|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -4, - -4, - -2 - ], - "y": [ - 174, - 188, - 188 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|4|2|2|2|-|-|-|-|-|-|-|-|-
-|-|-|2|2|0|2|2|0|2|2|2|2|0|2|2|4|-|-|-|-|-|-|-|-|-|-|-|-", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|4|2|2|2|-|-|-|-|-|-|-|-|-
-|-|-|2|2|0|2|2|0|2|2|2|2|0|2|2|4|-|-|-|-|-|-|-|-|-|-|-|-", - "BRANCH
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-|-|-|2|2|0|2|2|0|2|2|2|2|0|2|2|4|-|-|-|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 188, - 187, - 187 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
-|-|-|2|2|0|2|2|0|2|2|2|2|0|2|2|4|-|-|-|-|-|-|-|-|-|-|-|-
L37.AGCTCCTGTTTCCACC-1", - "BRANCH
-|-|-|2|2|0|2|2|0|2|2|2|2|0|2|2|4|-|-|-|-|-|-|-|-|-|-|-|-
L37.AGCTCCTGTTTCCACC-1", - "BRANCH
-|-|-|2|2|0|2|2|0|2|2|2|2|0|2|2|4|-|-|-|-|-|-|-|-|-|-|-|-
L37.AGCTCCTGTTTCCACC-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 187, - 187, - 187 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|2|2|0|2|2|0|2|2|2|2|0|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|0|2|2|2|2|0|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|0|2|2|2|2|0|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 188, - 188, - 188 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|-|-|-|2|2|2|-|-|-|-|-|-|-|-|-
L37.ACTGTCCTCACGATGT-1", - "BRANCH
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L37.ACTGTCCTCACGATGT-1", - "BRANCH
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L37.ACTGTCCTCACGATGT-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 188, - 188, - 188 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|4|2|2|2|-|-|-|-|-|-|-|-|-
L37.TGGGCGTCACGCCAGT-1", - "BRANCH
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L37.TGGGCGTCACGCCAGT-1", - "BRANCH
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L37.TGGGCGTCACGCCAGT-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 188, - 189, - 189 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|2|2|0|2|2|4|2|2|2|2|0|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|4|2|2|2|2|0|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|4|2|2|2|2|0|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -4, - -4, - -3 - ], - "y": [ - 174, - 191.5, - 191.5 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|2|2|0|2|2|4|2|2|2|-|-|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|4|2|2|2|-|-|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|4|2|2|2|-|-|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -3, - -3, - -2 - ], - "y": [ - 191.5, - 190.5, - 190.5 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|4|2|2|2|-|-|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-
2|2|2|2|2|0|2|2|4|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|4|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|4|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 190.5, - 190, - 190 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|4|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CGAGCCAAGCTGCAAG-1", - "BRANCH
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L37.CGAGCCAAGCTGCAAG-1", - "BRANCH
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L37.CGAGCCAAGCTGCAAG-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 190, - 190, - 190 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|4|2|2|2|-|-|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-
2|2|2|2|2|11|2|2|4|2|2|2|-|-|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|11|2|2|4|2|2|2|-|-|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|11|2|2|4|2|2|2|-|-|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 190.5, - 191, - 191 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|11|2|2|4|2|2|2|-|-|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-
L37.GCATGATGTCACCTAA-1", - "BRANCH
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L37.GCATGATGTCACCTAA-1", - "BRANCH
2|2|2|2|2|11|2|2|4|2|2|2|-|-|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-
L37.GCATGATGTCACCTAA-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 191, - 191, - 191 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|2|2|0|2|2|4|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|4|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CTTACCGGTGGTCCGT-1", - "BRANCH
2|2|2|2|2|0|2|2|4|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.CTTACCGGTGGTCCGT-1", - "BRANCH
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L37.CTTACCGGTGGTCCGT-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 192.5, - 192, - 192 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.CGAGCCATCTTCAACT-1", - "BRANCH
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L37.CGAGCCATCTTCAACT-1", - "BRANCH
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L37.CGAGCCATCTTCAACT-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 192.5, - 193, - 193 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.TGCTGCTGTATATCCG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-
L37.TGCTGCTGTATATCCG-1", - "BRANCH
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L37.TGCTGCTGTATATCCG-1" - ], - "type": "scatter", - "x": [ - -4, - -4, - 0 - ], - "y": [ - 174, - 194, - 194 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-
L37.TGCCAAACAAACGTGG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-
L37.TGCCAAACAAACGTGG-1", - "BRANCH
2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|5|6|3|-|-|-|-|-|-
L37.TGCCAAACAAACGTGG-1" - ], - "type": "scatter", - "x": [ - -4, - -4, - 0 - ], - "y": [ - 174, - 195, - 195 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|0|2|0|2|2|0|2|2|2|0|0|2|2|0|2|2|0|0|4|0|3|3|0|0|0|0", - "BRANCH
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2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|3|4|4|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|3|4|4|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|3|4|4|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|0|2|2|0|2|2|2|-|-|2|2|0|2|2|2|3|4|4|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -4, - -4, - -2 - ], - "y": [ - 199, - 196, - 196 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|2|2|16|2|2|0|2|2|2|-|-|2|2|0|2|2|2|3|4|4|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|16|2|2|0|2|2|2|-|-|2|2|0|2|2|2|3|4|4|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|16|2|2|0|2|2|2|-|-|2|2|0|2|2|2|3|4|4|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 196, - 196, - 196 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|16|2|2|0|2|2|2|-|-|2|2|0|2|2|2|3|4|4|-|-|-|-|-|-
L37.CGTTAGAAGCAGATCG-1", - "BRANCH
2|2|2|2|2|16|2|2|0|2|2|2|-|-|2|2|0|2|2|2|3|4|4|-|-|-|-|-|-
L37.CGTTAGAAGCAGATCG-1", - "BRANCH
2|2|2|2|2|16|2|2|0|2|2|2|-|-|2|2|0|2|2|2|3|4|4|-|-|-|-|-|-
L37.CGTTAGAAGCAGATCG-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 196, - 196, - 196 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|2|2|2|2|2|0|2|2|2|2|0|2|2|0|2|2|2|3|4|4|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|2|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|2|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|2|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-", - "BRANCH
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2|2|2|2|2|2|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 198.25, - 197.5, - 197.5 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|2|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TTGTAGGTCGAGAGCA-1", - "BRANCH
2|2|2|2|2|2|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TTGTAGGTCGAGAGCA-1", - "BRANCH
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L37.TTGTAGGTCGAGAGCA-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 197.5, - 197, - 197 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|2|2|2|0|2|2|2|-|-|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.GATTCAGGTATCAGTC-1", - "BRANCH
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L37.GATTCAGGTATCAGTC-1", - "BRANCH
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L37.GATTCAGGTATCAGTC-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 197.5, - 198, - 198 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|2|2|2|2|2|0|2|2|2|2|0|2|2|0|2|2|2|-|-|-|-|-|-|-|-|-
L37.TTTGCGCTCCGTCAAA-1", - "BRANCH
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L37.TTTGCGCTCCGTCAAA-1", - "BRANCH
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L37.TTTGCGCTCCGTCAAA-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 198.25, - 199, - 199 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.CCATTCGAGTCATGCT-1", - "BRANCH
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L37.CCATTCGAGTCATGCT-1", - "BRANCH
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L37.CCATTCGAGTCATGCT-1" - ], - "type": "scatter", - "x": [ - -3, - -3, - 0 - ], - "y": [ - 199.125, - 200, - 200 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.GTTCTCGAGTGGTCCC-1", - "BRANCH
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L37.GTTCTCGAGTGGTCCC-1", - "BRANCH
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L37.GTTCTCGAGTGGTCCC-1" - ], - "type": "scatter", - "x": [ - -4, - -4, - 0 - ], - "y": [ - 199, - 201, - 201 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.GTAACGTCACTAGTAC-1", - "BRANCH
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L37.GTAACGTCACTAGTAC-1", - "BRANCH
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L37.GTAACGTCACTAGTAC-1" - ], - "type": "scatter", - "x": [ - -4, - -4, - 0 - ], - "y": [ - 199, - 202, - 202 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|2|2|0|2|2|0|2|2|2|2|0|2|2|0|2|2|2|6|4|4|-|-|-|-|-|-", - "BRANCH
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L37.CTCGAGGGTACATCCA-1", - "BRANCH
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L37.CTCGAGGGTACATCCA-1", - "BRANCH
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L37.CTCGAGGGTACATCCA-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 203, - 203, - 203 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.TAAACCGTCGTACCGG-1", - "BRANCH
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L37.TAAACCGTCGTACCGG-1", - "BRANCH
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L37.TAAACCGTCGTACCGG-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 204.5, - 204, - 204 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.CTTAGGATCTGAAAGA-1", - "BRANCH
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L37.CTTAGGATCTGAAAGA-1", - "BRANCH
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L37.CTTAGGATCTGAAAGA-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 204.5, - 205, - 205 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.ATAGACCTCATCACCC-1", - "BRANCH
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L37.ATAGACCTCATCACCC-1", - "BRANCH
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L37.ATAGACCTCATCACCC-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 205.25, - 206, - 206 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.TTCTCAAGTGTAATGA-1", - "BRANCH
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L37.TTCTCAAGTGTAATGA-1", - "BRANCH
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2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|0" - ], - "type": "scatter", - "x": [ - -3, - -3, - -2 - ], - "y": [ - 211.5, - 210, - 210 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|5|2|4|2|2|15|2|2|2|-|-|2|2|0|2|2|11|-|-|-|-|-|-|3|3|0", - "BRANCH
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2|2|2|5|2|4|2|2|15|2|2|2|-|-|2|2|0|2|2|11|-|-|-|-|-|-|3|3|0", - "BRANCH
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2|2|2|5|2|4|2|2|15|2|2|2|-|-|2|2|0|2|2|11|-|-|-|-|-|-|3|3|0" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 210, - 208, - 208 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|15|2|2|2|-|-|2|2|0|2|2|11|-|-|-|-|-|-|3|3|0
L37.CGAGAAGCACACGCTG-1", - "BRANCH
2|2|2|5|2|4|2|2|15|2|2|2|-|-|2|2|0|2|2|11|-|-|-|-|-|-|3|3|0
L37.CGAGAAGCACACGCTG-1", - "BRANCH
2|2|2|5|2|4|2|2|15|2|2|2|-|-|2|2|0|2|2|11|-|-|-|-|-|-|3|3|0
L37.CGAGAAGCACACGCTG-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 208, - 208, - 208 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|0
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|14|-|-|-|-|-|-|3|3|0", - "BRANCH
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2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|14|-|-|-|-|-|-|3|3|0", - "BRANCH
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2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|14|-|-|-|-|-|-|3|3|0" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 210, - 209, - 209 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|14|-|-|-|-|-|-|3|3|0
L37.ATGAGGGCAATCGAAA-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|14|-|-|-|-|-|-|3|3|0
L37.ATGAGGGCAATCGAAA-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|14|-|-|-|-|-|-|3|3|0
L37.ATGAGGGCAATCGAAA-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 209, - 209, - 209 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|0
L37.TCAGGTAAGGCATTGG-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|0
L37.TCAGGTAAGGCATTGG-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|0
L37.TCAGGTAAGGCATTGG-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 210, - 210, - 210 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|0
L37.CGTCACTTCATCATTC-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|0
L37.CGTCACTTCATCATTC-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|0
L37.CGTCACTTCATCATTC-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 210, - 211, - 211 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|0
L37.AGCTCCTAGTGTACGG-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|0
L37.AGCTCCTAGTGTACGG-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|0
L37.AGCTCCTAGTGTACGG-1" - ], - "type": "scatter", - "x": [ - -2, - -2, - 0 - ], - "y": [ - 210, - 212, - 212 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.CTTACCGTCTACTATC-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|0|4|2|2|0|2|2|0|-|-|-|-|-|-|3|3|0
L37.CTTACCGTCTACTATC-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|0|4|2|2|0|2|2|0|-|-|-|-|-|-|3|3|0
L37.CTTACCGTCTACTATC-1" - ], - "type": "scatter", - "x": [ - -3, - -3, - 0 - ], - "y": [ - 211.5, - 213, - 213 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|3|3|0|3|3|0", - "BRANCH
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2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|3|3|0|3|3|0
L37.GTAACGTTCATATCGG-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|3|3|0|3|3|0
L37.GTAACGTTCATATCGG-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|3|3|0|3|3|0
L37.GTAACGTTCATATCGG-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 215, - 214, - 214 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|3|3|0|3|3|0
L37.ATTGGACCACAGAGGT-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|3|3|0|3|3|0
L37.ATTGGACCACAGAGGT-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|3|3|0|3|3|0
L37.ATTGGACCACAGAGGT-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 215, - 215, - 215 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|3|3|0|3|3|0
L37.AAACCTGTCCTTAATC-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|3|3|0|3|3|0
L37.AAACCTGTCCTTAATC-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|3|3|0|3|3|0
L37.AAACCTGTCCTTAATC-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 215, - 216, - 216 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|0|4|2|2|0|2|2|0|-|-|-|3|3|0|3|3|0
2|2|2|5|2|4|2|2|0|2|2|2|0|4|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20", - "BRANCH
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2|2|2|5|2|4|2|2|8|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20", - "BRANCH
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2|2|2|5|2|4|2|2|8|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20" - ], - "type": "scatter", - "x": [ - -4, - -4, - -2 - ], - "y": [ - 218, - 217, - 217 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|8|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20
2|2|2|5|2|4|2|2|8|2|2|2|-|-|2|2|5|2|2|0|-|-|-|-|-|-|3|3|20", - "BRANCH
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2|2|2|5|2|4|2|2|8|2|2|2|-|-|2|2|5|2|2|0|-|-|-|-|-|-|3|3|20", - "BRANCH
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2|2|2|5|2|4|2|2|8|2|2|2|-|-|2|2|5|2|2|0|-|-|-|-|-|-|3|3|20" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 217, - 217, - 217 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|8|2|2|2|-|-|2|2|5|2|2|0|-|-|-|-|-|-|3|3|20
L37.CATCAAGCATCCAACA-1", - "BRANCH
2|2|2|5|2|4|2|2|8|2|2|2|-|-|2|2|5|2|2|0|-|-|-|-|-|-|3|3|20
L37.CATCAAGCATCCAACA-1", - "BRANCH
2|2|2|5|2|4|2|2|8|2|2|2|-|-|2|2|5|2|2|0|-|-|-|-|-|-|3|3|20
L37.CATCAAGCATCCAACA-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 217, - 217, - 217 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|0|4|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20", - "BRANCH
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2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20", - "BRANCH
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2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20" - ], - "type": "scatter", - "x": [ - -4, - -4, - -3 - ], - "y": [ - 218, - 218, - 218 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20
2|2|2|-|-|-|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20", - "BRANCH
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2|2|2|-|-|-|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20", - "BRANCH
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2|2|2|-|-|-|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20" - ], - "type": "scatter", - "x": [ - -3, - -3, - -2 - ], - "y": [ - 218, - 218, - 218 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|-|-|-|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20
2|2|2|-|-|-|-|-|-|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20", - "BRANCH
2|2|2|-|-|-|2|2|0|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20
2|2|2|-|-|-|-|-|-|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20", - "BRANCH
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2|2|2|-|-|-|-|-|-|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20" - ], - "type": "scatter", - "x": [ - -2, - -2, - -1 - ], - "y": [ - 218, - 218, - 218 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|-|-|-|-|-|-|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20
L37.AATCCAGCACCAGTTA-1", - "BRANCH
2|2|2|-|-|-|-|-|-|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20
L37.AATCCAGCACCAGTTA-1", - "BRANCH
2|2|2|-|-|-|-|-|-|2|2|2|-|-|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20
L37.AATCCAGCACCAGTTA-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 218, - 218, - 218 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|0|4|2|2|0|2|2|0|-|-|-|-|-|-|3|3|20
2|2|2|5|2|4|2|2|0|2|2|2|0|4|2|2|11|2|2|0|-|-|-|-|-|-|3|3|20", - "BRANCH
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2|2|2|5|2|4|2|2|0|2|2|2|0|4|2|2|11|2|2|0|-|-|-|-|-|-|3|3|20", - "BRANCH
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2|2|2|5|2|4|2|2|0|2|2|2|0|4|2|2|11|2|2|0|-|-|-|-|-|-|3|3|20
L37.GCTTCCATCTGTTTGT-1", - "BRANCH
2|2|2|5|2|4|2|2|0|2|2|2|0|4|2|2|11|2|2|0|-|-|-|-|-|-|3|3|20
L37.GCTTCCATCTGTTTGT-1", - "BRANCH
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L37.GCTTCCATCTGTTTGT-1" - ], - "type": "scatter", - "x": [ - -1, - -1, - 0 - ], - "y": [ - 219, - 219, - 219 - ] - }, - { - "hoverinfo": "text", - "line": { - "color": "red", - "width": 1 - }, - "mode": "lines", - "showlegend": false, - "text": [ - "BRANCH
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L37.TCGCGAGTCAGCTCGG-1", - "BRANCH
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L37.GCATGATGTCGAGATG-1", - "BRANCH
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L37.TGGTTAGAGCCTCGTG-1", - "BRANCH
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L37.TCATTACAGGCATGGT-1", - "BRANCH
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L37.GTGCTTCAGCGTGAGT-1", - "BRANCH
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L37.GTGCTTCAGCGTGAGT-1", - "BRANCH
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L37.GGCAATTCATCCCACT-1", - "BRANCH
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L37.GGCAATTCATCCCACT-1", - "BRANCH
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L37.GCATGATCAAGGCTCC-1", - "BRANCH
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L37.GCATGATCAAGGCTCC-1", - "BRANCH
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L37.GTTCTCGTCTGCGGCA-1", - "BRANCH
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L37.GCTGCTTCATCGGAAG-1", - "BRANCH
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L37.GCTGCTTCATCGGAAG-1", - "BRANCH
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L37.GTCGGGTCACCGTTGG-1", - "BRANCH
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L37.GTCGGGTCACCGTTGG-1", - "BRANCH
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L37.CGATCGGGTCTAACGT-1", - "BRANCH
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L37.CGATCGGGTCTAACGT-1", - "BRANCH
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L37.CATCGGGAGACACGAC-1", - "BRANCH
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L37.CATCGGGAGACACGAC-1", - "BRANCH
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
CCGAA[113:44D]TTAAC", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 23.5, - 23.5, - 22.5, - 22.5, - 23.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#2fb903", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTATTATCCGCATCT-1
ACGTGCAAGGACGC_r1
CCGAA[113:44D]TTAAC", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 21.5, - 21.5, - 20.5, - 20.5, - 21.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGGGAGTCAGCGACC-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 223.5, - 223.5, - 222.5, - 222.5, - 223.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCCACCCAGCCAGAA-1
ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 215.5, - 215.5, - 214.5, - 214.5, - 215.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACGTTCATATCGG-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 214.5, - 214.5, - 213.5, - 213.5, - 214.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTACCGTCTACTATC-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 213.5, - 213.5, - 212.5, - 212.5, - 213.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCGAGGGTACATCCA-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 203.5, - 203.5, - 202.5, - 202.5, - 203.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACGTCACTAGTAC-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 202.5, - 202.5, - 201.5, - 201.5, - 202.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGAGTGGTCCC-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 201.5, - 201.5, - 200.5, - 200.5, - 201.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATGTCACCTAA-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 191.5, - 191.5, - 190.5, - 190.5, - 191.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCAAGCTGCAAG-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 190.5, - 190.5, - 189.5, - 189.5, - 190.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGATGTTTCCGGCACA-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 150.5, - 150.5, - 149.5, - 149.5, - 150.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGGAGATGTCAATACC-1
ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 144.5, - 144.5, - 143.5, - 143.5, - 144.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGAGTCGATAA-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 139.5, - 139.5, - 138.5, - 138.5, - 139.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATCTGAGGCTACGA-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 138.5, - 138.5, - 137.5, - 137.5, - 138.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAGGAGTGCACTTA-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 137.5, - 137.5, - 136.5, - 136.5, - 137.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCTGTGTATGAAAC-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 136.5, - 136.5, - 135.5, - 135.5, - 136.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGACATTAGGAGTCTG-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 135.5, - 135.5, - 134.5, - 134.5, - 135.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGGAATGAGCCACCTG-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 134.5, - 134.5, - 133.5, - 133.5, - 134.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCCGATCACCGAATT-1
ACGTGCAAGGACGC_r1
ACGCC[110:12D]GACAA", - "type": "scatter", - "x": [ - 1.2000000000000002, - 0.4, - 0.4, - 1.2000000000000002, - 1.2000000000000002 - ], - "y": [ - 133.5, - 133.5, - 132.5, - 132.5, - 133.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#935728", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTATTGGTGAGTATA-1
ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r1
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ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 146.5, - 146.5, - 145.5, - 145.5, - 146.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCAGAGGACGG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 142.5, - 142.5, - 141.5, - 141.5, - 142.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCCTAAGCTGCGAA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 140.5, - 140.5, - 139.5, - 139.5, - 140.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTGAAACCATTTGCCC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 131.5, - 131.5, - 130.5, - 130.5, - 131.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGAGCTTTCAAGATCC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 130.5, - 130.5, - 129.5, - 129.5, - 130.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCACCTAGTACACCT-1
ACGTGCAAGGACGC_r2
nan", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 129.5, - 129.5, - 128.5, - 128.5, - 129.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCAATTTCACCTCGT-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 128.5, - 128.5, - 127.5, - 127.5, - 128.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTCAGTTCGAGAGCA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 124.5, - 124.5, - 123.5, - 123.5, - 124.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTGGTGAGGCCGAAT-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 121.5, - 121.5, - 120.5, - 120.5, - 121.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#cf2ee2", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATTATCCAGGCGATA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCTCTGGT[174:48D]GGAGG", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 113.5, - 113.5, - 112.5, - 112.5, - 113.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGTGTGCACGGTGTC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 112.5, - 112.5, - 111.5, - 111.5, - 112.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGACCAAGGAATTAC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 111.5, - 111.5, - 110.5, - 110.5, - 111.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGTGAGGTCGGTCTAA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 110.5, - 110.5, - 109.5, - 109.5, - 110.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTACATTGTTCGCGAC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 109.5, - 109.5, - 108.5, - 108.5, - 109.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGTGACAGGGTTAG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 103.5, - 103.5, - 102.5, - 102.5, - 103.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCTGGTAGGTGCTTT-1
ACGTGCAAGGACGC_r2
nan", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 102.5, - 102.5, - 101.5, - 101.5, - 102.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATCGAAGTTTGTTTC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 96.5, - 96.5, - 95.5, - 95.5, - 96.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATAGTCTACTTAC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 94.5, - 94.5, - 93.5, - 93.5, - 94.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#2fb903", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGCAGCGTGTGCGTC-1
ACGTGCAAGGACGC_r2
CCGAA[113:44D]TTAAC", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 36.5, - 36.5, - 35.5, - 35.5, - 36.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#2fb903", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCAATCCTCCTTTACA-1
ACGTGCAAGGACGC_r2
CCGAA[113:44D]TTAAC", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 35.5, - 35.5, - 34.5, - 34.5, - 35.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#2fb903", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACAGCCGAGGTAGCCA-1
ACGTGCAAGGACGC_r2
CCGAA[113:44D]TTAAC", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 23.5, - 23.5, - 22.5, - 22.5, - 23.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#2fb903", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTATTATCCGCATCT-1
ACGTGCAAGGACGC_r2
CCGAA[113:44D]TTAAC", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 21.5, - 21.5, - 20.5, - 20.5, - 21.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGGGAGTCAGCGACC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 223.5, - 223.5, - 222.5, - 222.5, - 223.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCCACCCAGCCAGAA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 222.5, - 222.5, - 221.5, - 221.5, - 222.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AAACCTGTCCTTAATC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 216.5, - 216.5, - 215.5, - 215.5, - 216.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTGGACCACAGAGGT-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 215.5, - 215.5, - 214.5, - 214.5, - 215.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACGTTCATATCGG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 214.5, - 214.5, - 213.5, - 213.5, - 214.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTACCGTCTACTATC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 213.5, - 213.5, - 212.5, - 212.5, - 213.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCGAGGGTACATCCA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 203.5, - 203.5, - 202.5, - 202.5, - 203.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACGTCACTAGTAC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 202.5, - 202.5, - 201.5, - 201.5, - 202.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGAGTGGTCCC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 201.5, - 201.5, - 200.5, - 200.5, - 201.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATGTCACCTAA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 191.5, - 191.5, - 190.5, - 190.5, - 191.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCAAGCTGCAAG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 190.5, - 190.5, - 189.5, - 189.5, - 190.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGATGTTTCCGGCACA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 150.5, - 150.5, - 149.5, - 149.5, - 150.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGGAGATGTCAATACC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 148.5, - 148.5, - 147.5, - 147.5, - 148.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCTACTAGCTCTCGG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 144.5, - 144.5, - 143.5, - 143.5, - 144.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGAGTCGATAA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 139.5, - 139.5, - 138.5, - 138.5, - 139.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATCTGAGGCTACGA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 138.5, - 138.5, - 137.5, - 137.5, - 138.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAGGAGTGCACTTA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 137.5, - 137.5, - 136.5, - 136.5, - 137.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCTGTGTATGAAAC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 136.5, - 136.5, - 135.5, - 135.5, - 136.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGACATTAGGAGTCTG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 135.5, - 135.5, - 134.5, - 134.5, - 135.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGGAATGAGCCACCTG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 134.5, - 134.5, - 133.5, - 133.5, - 134.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCCGATCACCGAATT-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 133.5, - 133.5, - 132.5, - 132.5, - 133.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTATTGGTGAGTATA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 132.5, - 132.5, - 131.5, - 131.5, - 132.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATGGTAGGACCACA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 127.5, - 127.5, - 126.5, - 126.5, - 127.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAGCAACACCATGTA-1
ACGTGCAAGGACGC_r2
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ACGTGCAAGGACGC_r2
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ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 115.5, - 115.5, - 114.5, - 114.5, - 115.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTAGTTCAGTGACATA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 93.5, - 93.5, - 92.5, - 92.5, - 93.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCACTCTTCGAGAGCA-1
ACGTGCAAGGACGC_r2
nan", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 92.5, - 92.5, - 91.5, - 91.5, - 92.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATACGGTTCCCGAG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 91.5, - 91.5, - 90.5, - 90.5, - 91.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTTTCTTCACAACGT-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 90.5, - 90.5, - 89.5, - 89.5, - 90.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTTCTAGTACTTCTT-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 89.5, - 89.5, - 88.5, - 88.5, - 89.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGTGGTAGTGTTGAGG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 87.5, - 87.5, - 86.5, - 86.5, - 87.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTCTAAGTTTGGGCC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 86.5, - 86.5, - 85.5, - 85.5, - 86.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGAATCGTTATCGGT-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 83.5, - 83.5, - 82.5, - 82.5, - 83.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#2fb903", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTGCTTCATCGGAAG-1
ACGTGCAAGGACGC_r2
CCGAA[113:44D]TTAAC", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 68.5, - 68.5, - 67.5, - 67.5, - 68.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#2fb903", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACCTTAGTCTCCATC-1
ACGTGCAAGGACGC_r2
CCGAA[113:44D]TTAAC", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 54.5, - 54.5, - 53.5, - 53.5, - 54.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#2fb903", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTTCGGCACAGGTTT-1
ACGTGCAAGGACGC_r2
CCGAA[113:44D]TTAAC", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 37.5, - 37.5, - 36.5, - 36.5, - 37.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTCTCAAGTGTAATGA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 207.5, - 207.5, - 206.5, - 206.5, - 207.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAGGATCTGAAAGA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 205.5, - 205.5, - 204.5, - 204.5, - 205.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TAAACCGTCGTACCGG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 204.5, - 204.5, - 203.5, - 203.5, - 204.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCATCTTCAACT-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 193.5, - 193.5, - 192.5, - 192.5, - 193.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTACCGGTGGTCCGT-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 192.5, - 192.5, - 191.5, - 191.5, - 192.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGTCCTCACGATGT-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 188.5, - 188.5, - 187.5, - 187.5, - 188.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCTCCTGTTTCCACC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 187.5, - 187.5, - 186.5, - 186.5, - 187.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGCGATAGTAGGTGC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 157.5, - 157.5, - 156.5, - 156.5, - 157.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGCCGCATGCAATC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 156.5, - 156.5, - 155.5, - 155.5, - 156.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCAAACTTCACGCATA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 155.5, - 155.5, - 154.5, - 154.5, - 155.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAAGTTCAACCA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 154.5, - 154.5, - 153.5, - 153.5, - 154.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTGGGGTTCACGGC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 152.5, - 152.5, - 151.5, - 151.5, - 152.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGGTATGAATG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 151.5, - 151.5, - 150.5, - 150.5, - 151.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTCACTAGTCCAGGA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 149.5, - 149.5, - 148.5, - 148.5, - 149.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCATTCGAGCGATGAC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 126.5, - 126.5, - 125.5, - 125.5, - 126.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACTCATTCGGTTCGG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 125.5, - 125.5, - 124.5, - 124.5, - 125.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGAATCCAAGGCTCC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 123.5, - 123.5, - 122.5, - 122.5, - 123.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCAGGCAATTA-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 120.5, - 120.5, - 119.5, - 119.5, - 120.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAGGAGGAGTTGC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 119.5, - 119.5, - 118.5, - 118.5, - 119.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCACACAAGCCC-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 116.5, - 116.5, - 115.5, - 115.5, - 116.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AACCGCGTCCACTGGG-1
ACGTGCAAGGACGC_r2
nan", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 88.5, - 88.5, - 87.5, - 87.5, - 88.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCCTCTAGTGCTGTAT-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 85.5, - 85.5, - 84.5, - 84.5, - 85.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTCTGCATTCCTCG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 84.5, - 84.5, - 83.5, - 83.5, - 84.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCATGGGTCACAAGG-1
ACGTGCAAGGACGC_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 82.5, - 82.5, - 81.5, - 81.5, - 82.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#2fb903", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCACACGTAGCGTAG-1
ACGTGCAAGGACGC_r2
CCGAA[113:44D]TTAAC", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 65.5, - 65.5, - 64.5, - 64.5, - 65.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#2fb903", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAGACCTCATATCGG-1
ACGTGCAAGGACGC_r2
CCGAA[113:44D]TTAAC", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 58.5, - 58.5, - 57.5, - 57.5, - 58.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#2fb903", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCATAGTTACGCGC-1
ACGTGCAAGGACGC_r2
CCGAA[113:44D]TTAAC", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 57.5, - 57.5, - 56.5, - 56.5, - 57.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#2fb903", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCATTACAGGCATGGT-1
ACGTGCAAGGACGC_r2
CCGAA[113:44D]TTAAC", - "type": "scatter", - "x": [ - 2.4, - 1.6, - 1.6, - 2.4, - 2.4 - ], - "y": [ - 56.5, - 56.5, - 55.5, - 55.5, - 56.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#2fb903", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTAGAGCCTCGTG-1
ACGTGCAAGGACGC_r2
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ACGTGCAAGGACGC_r2
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ACGTGCAAGGACGC_r2
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ACGTGCAAGGACGC_r2
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ACGTGCAAGGACGC_r2
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ACGTGCAAGGACGC_r2
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ACGTGCAAGGACGC_r2
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ACGTGCAAGGACGC_r2
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ACGTGCAAGGACGC_r2
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
ATTCG[221:1D]GGAGG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 131.5, - 131.5, - 130.5, - 130.5, - 131.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4444ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGAGCTTTCAAGATCC-1
ACGTGCAAGGACGC_r3
ATTCG[221:1D]GGAGG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 130.5, - 130.5, - 129.5, - 129.5, - 130.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCACCTAGTACACCT-1
ACGTGCAAGGACGC_r3
nan", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 129.5, - 129.5, - 128.5, - 128.5, - 129.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4444ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCAATTTCACCTCGT-1
ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
CTGGT[174:48D]GGAGG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 113.5, - 113.5, - 112.5, - 112.5, - 113.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#e6bf1d", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGTGTGCACGGTGTC-1
ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
ATTCG[221:1D]GGAGG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 203.5, - 203.5, - 202.5, - 202.5, - 203.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4444ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACGTCACTAGTAC-1
ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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ACGTGCAAGGACGC_r3
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CTATT[215:6D]CGGAG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 14.5, - 14.5, - 13.5, - 13.5, - 14.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGAGGATCCAGATCA-1
ACGTGCAAGGACGC_r3
ATTCG[None]", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 13.5, - 13.5, - 12.5, - 12.5, - 13.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#3dad4f", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACTGTCCAGTATG-1
ACGTGCAAGGACGC_r3
ATTAA[225:11D]TTCGT", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 12.5, - 12.5, - 11.5, - 11.5, - 12.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTCCGTAGCTCCG-1
ACGTGCAAGGACGC_r3
nan", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 11.5, - 11.5, - 10.5, - 10.5, - 11.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d174cf", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCGTACGTCCGTCAG-1
ACGTGCAAGGACGC_r3
ATTAA[217:5D]GGAGG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 5.5, - 5.5, - 4.5, - 4.5, - 5.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4444ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCAGAAGTCGCATCG-1
ACGTGCAAGGACGC_r3
ATTCG[221:1D]GGAGG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 79.5, - 79.5, - 78.5, - 78.5, - 79.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b29a13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTTCTGAGTTAAGTG-1
ACGTGCAAGGACGC_r3
TGTCA[196:30D]GACAA", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 15.5, - 15.5, - 14.5, - 14.5, - 15.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b451d4", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGGGTCCAGCTGCAC-1
ACGTGCAAGGACGC_r3
TGGCG[86:132D]TCGCG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 3.5, - 3.5, - 2.5, - 2.5, - 3.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4444ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGCCAAAGCCTTGAT-1
ACGTGCAAGGACGC_r3
ATTCG[221:1D]GGAGG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 81.5, - 81.5, - 80.5, - 80.5, - 81.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4444ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAAACCATAAAGGT-1
ACGTGCAAGGACGC_r3
ATTCG[221:1D]GGAGG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 80.5, - 80.5, - 79.5, - 79.5, - 80.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4444ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATTTGTTGCCTCT-1
ACGTGCAAGGACGC_r3
ATTCG[221:1D]GGAGG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 78.5, - 78.5, - 77.5, - 77.5, - 78.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATATTCCAGGGTTAG-1
ACGTGCAAGGACGC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 16.5, - 16.5, - 15.5, - 15.5, - 16.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTAGAAGAATGTGT-1
ACGTGCAAGGACGC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 4.5, - 4.5, - 3.5, - 3.5, - 4.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#fd9d76", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATTCGCTCCCACTTG-1
ACGTGCAAGGACGC_r3
AATTC[220:1I]GGCGGA", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 1.5, - 1.5, - 0.5, - 0.5, - 1.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#cb53b4", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAATGGTCACCAGGC-1
ACGTGCAAGGACGC_r3
AATTC[220:3D]GAGGA", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 0.5, - 0.5, - -0.5, - -0.5, - 0.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b07186", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGGAACTCGGCTTGG-1
ACGTGCAAGGACGC_r3
GATAT[167:54D]CGGAG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 2.5, - 2.5, - 1.5, - 1.5, - 2.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGCCAGAGGCCATAG-1
ACGTGCAAGGACGC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 3.6, - 2.8000000000000003, - 2.8000000000000003, - 3.6, - 3.6 - ], - "y": [ - 77.5, - 77.5, - 76.5, - 76.5, - 77.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGGAGACAAGGGTCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 34.5, - 34.5, - 33.5, - 33.5, - 34.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCATTTCCAGGGCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 30.5, - 30.5, - 29.5, - 29.5, - 30.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCCTAATCTCAAACG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 29.5, - 29.5, - 28.5, - 28.5, - 29.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATGACACATCATCCC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 28.5, - 28.5, - 27.5, - 27.5, - 28.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AATCCAGCACCAGTTA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 218.5, - 218.5, - 217.5, - 217.5, - 218.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATTCAGGTATCAGTC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 198.5, - 198.5, - 197.5, - 197.5, - 198.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGTAGGTCGAGAGCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 197.5, - 197.5, - 196.5, - 196.5, - 197.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTACAGGTGTTTGTG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 107.5, - 107.5, - 106.5, - 106.5, - 107.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGAGCAACGGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 105.5, - 105.5, - 104.5, - 104.5, - 105.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGCCAGAGGTCATCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 101.5, - 101.5, - 100.5, - 100.5, - 101.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TATCAGGTCACGATGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 100.5, - 100.5, - 99.5, - 99.5, - 100.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGACTTAGCTAAGAT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 98.5, - 98.5, - 97.5, - 97.5, - 98.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GAACGGAAGGCAGGTT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 97.5, - 97.5, - 96.5, - 96.5, - 97.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCTCTCGTTTA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 33.5, - 33.5, - 32.5, - 32.5, - 33.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AATCGGTTCAGGCAAG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 32.5, - 32.5, - 31.5, - 31.5, - 32.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGTAACAGCGATATA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 31.5, - 31.5, - 30.5, - 30.5, - 31.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GACACGCCAATAAGCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 27.5, - 27.5, - 26.5, - 26.5, - 27.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGGGACATAGAAAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 26.5, - 26.5, - 25.5, - 25.5, - 26.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCTGTTCGAGAGCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 25.5, - 25.5, - 24.5, - 24.5, - 25.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACCAGTACAAGGACTG-1
CTCTTTTCAGTTCA_r1
nan", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 24.5, - 24.5, - 23.5, - 23.5, - 24.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCACGAAAGGATATAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 224.5, - 224.5, - 223.5, - 223.5, - 224.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAACCTCAGACAGGCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 221.5, - 221.5, - 220.5, - 220.5, - 221.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACGGGCTCGTCTGAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 220.5, - 220.5, - 219.5, - 219.5, - 220.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATCAAGCATCCAACA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 217.5, - 217.5, - 216.5, - 216.5, - 217.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGAGGGCAATCGAAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 209.5, - 209.5, - 208.5, - 208.5, - 209.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGAAGCACACGCTG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 208.5, - 208.5, - 207.5, - 207.5, - 208.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTTGCGCTCCGTCAAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 199.5, - 199.5, - 198.5, - 198.5, - 199.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTAGAAGCAGATCG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 196.5, - 196.5, - 195.5, - 195.5, - 196.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAACAGGTGCCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 145.5, - 145.5, - 144.5, - 144.5, - 145.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AAAGATGGTAGCGTGA-1
CTCTTTTCAGTTCA_r1
nan", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 143.5, - 143.5, - 142.5, - 142.5, - 143.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCACAAGAGTGAACGC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 141.5, - 141.5, - 140.5, - 140.5, - 141.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCGTACTTCTT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 108.5, - 108.5, - 107.5, - 107.5, - 108.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGATGTTAGAACTGTA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 106.5, - 106.5, - 105.5, - 105.5, - 106.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTCTGACAAACGTGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 104.5, - 104.5, - 103.5, - 103.5, - 104.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCTAATCAGGCCCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 99.5, - 99.5, - 98.5, - 98.5, - 99.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GACGCGTGTGCAGTAG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 95.5, - 95.5, - 94.5, - 94.5, - 95.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTCATTTTCCTCAACC-1
CTCTTTTCAGTTCA_r1
nan", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 22.5, - 22.5, - 21.5, - 21.5, - 22.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCGCGAGTCAGCTCGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 225.5, - 225.5, - 224.5, - 224.5, - 225.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTCCATCTGTTTGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 219.5, - 219.5, - 218.5, - 218.5, - 219.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCTCCTAGTGTACGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 212.5, - 212.5, - 211.5, - 211.5, - 212.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTCACTTCATCATTC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 211.5, - 211.5, - 210.5, - 210.5, - 211.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAGGTAAGGCATTGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 210.5, - 210.5, - 209.5, - 209.5, - 210.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCATTCGAGTCATGCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 200.5, - 200.5, - 199.5, - 199.5, - 200.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGGGAGCAAAGGAAG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 147.5, - 147.5, - 146.5, - 146.5, - 147.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATATTCGTCGAATCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 146.5, - 146.5, - 145.5, - 145.5, - 146.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCAGAGGACGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 142.5, - 142.5, - 141.5, - 141.5, - 142.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCCTAAGCTGCGAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 140.5, - 140.5, - 139.5, - 139.5, - 140.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTGAAACCATTTGCCC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 131.5, - 131.5, - 130.5, - 130.5, - 131.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGAGCTTTCAAGATCC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 130.5, - 130.5, - 129.5, - 129.5, - 130.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCACCTAGTACACCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 129.5, - 129.5, - 128.5, - 128.5, - 129.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCAATTTCACCTCGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 128.5, - 128.5, - 127.5, - 127.5, - 128.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTCAGTTCGAGAGCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 124.5, - 124.5, - 123.5, - 123.5, - 124.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTGGTGAGGCCGAAT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 121.5, - 121.5, - 120.5, - 120.5, - 121.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATTATCCAGGCGATA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 113.5, - 113.5, - 112.5, - 112.5, - 113.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGTGTGCACGGTGTC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 112.5, - 112.5, - 111.5, - 111.5, - 112.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGACCAAGGAATTAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 111.5, - 111.5, - 110.5, - 110.5, - 111.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGTGAGGTCGGTCTAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 110.5, - 110.5, - 109.5, - 109.5, - 110.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTACATTGTTCGCGAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 109.5, - 109.5, - 108.5, - 108.5, - 109.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGTGACAGGGTTAG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 103.5, - 103.5, - 102.5, - 102.5, - 103.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCTGGTAGGTGCTTT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 102.5, - 102.5, - 101.5, - 101.5, - 102.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATCGAAGTTTGTTTC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 96.5, - 96.5, - 95.5, - 95.5, - 96.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATAGTCTACTTAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 94.5, - 94.5, - 93.5, - 93.5, - 94.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGCAGCGTGTGCGTC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 36.5, - 36.5, - 35.5, - 35.5, - 36.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCAATCCTCCTTTACA-1
CTCTTTTCAGTTCA_r1
nan", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 35.5, - 35.5, - 34.5, - 34.5, - 35.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACAGCCGAGGTAGCCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 23.5, - 23.5, - 22.5, - 22.5, - 23.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTATTATCCGCATCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 21.5, - 21.5, - 20.5, - 20.5, - 21.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGGGAGTCAGCGACC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 223.5, - 223.5, - 222.5, - 222.5, - 223.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCCACCCAGCCAGAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 222.5, - 222.5, - 221.5, - 221.5, - 222.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AAACCTGTCCTTAATC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 216.5, - 216.5, - 215.5, - 215.5, - 216.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTGGACCACAGAGGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 215.5, - 215.5, - 214.5, - 214.5, - 215.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACGTTCATATCGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 214.5, - 214.5, - 213.5, - 213.5, - 214.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTACCGTCTACTATC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 213.5, - 213.5, - 212.5, - 212.5, - 213.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCGAGGGTACATCCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 203.5, - 203.5, - 202.5, - 202.5, - 203.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACGTCACTAGTAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 202.5, - 202.5, - 201.5, - 201.5, - 202.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGAGTGGTCCC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 201.5, - 201.5, - 200.5, - 200.5, - 201.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATGTCACCTAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 191.5, - 191.5, - 190.5, - 190.5, - 191.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCAAGCTGCAAG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 190.5, - 190.5, - 189.5, - 189.5, - 190.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGATGTTTCCGGCACA-1
CTCTTTTCAGTTCA_r1
nan", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 150.5, - 150.5, - 149.5, - 149.5, - 150.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGGAGATGTCAATACC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 148.5, - 148.5, - 147.5, - 147.5, - 148.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCTACTAGCTCTCGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 144.5, - 144.5, - 143.5, - 143.5, - 144.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGAGTCGATAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 139.5, - 139.5, - 138.5, - 138.5, - 139.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATCTGAGGCTACGA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 138.5, - 138.5, - 137.5, - 137.5, - 138.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAGGAGTGCACTTA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 137.5, - 137.5, - 136.5, - 136.5, - 137.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCTGTGTATGAAAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 136.5, - 136.5, - 135.5, - 135.5, - 136.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGACATTAGGAGTCTG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 135.5, - 135.5, - 134.5, - 134.5, - 135.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGGAATGAGCCACCTG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 134.5, - 134.5, - 133.5, - 133.5, - 134.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCCGATCACCGAATT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 133.5, - 133.5, - 132.5, - 132.5, - 133.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTATTGGTGAGTATA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 132.5, - 132.5, - 131.5, - 131.5, - 132.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATGGTAGGACCACA-1
CTCTTTTCAGTTCA_r1
nan", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 127.5, - 127.5, - 126.5, - 126.5, - 127.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAGCAACACCATGTA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 122.5, - 122.5, - 121.5, - 121.5, - 122.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACACCAATCCACTGGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 118.5, - 118.5, - 117.5, - 117.5, - 118.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTGTGCTCAACTGGCC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 115.5, - 115.5, - 114.5, - 114.5, - 115.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTAGTTCAGTGACATA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 93.5, - 93.5, - 92.5, - 92.5, - 93.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCACTCTTCGAGAGCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 92.5, - 92.5, - 91.5, - 91.5, - 92.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATACGGTTCCCGAG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 91.5, - 91.5, - 90.5, - 90.5, - 91.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTTTCTTCACAACGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 90.5, - 90.5, - 89.5, - 89.5, - 90.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTTCTAGTACTTCTT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 89.5, - 89.5, - 88.5, - 88.5, - 89.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGTGGTAGTGTTGAGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 87.5, - 87.5, - 86.5, - 86.5, - 87.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTCTAAGTTTGGGCC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 86.5, - 86.5, - 85.5, - 85.5, - 86.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGAATCGTTATCGGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 83.5, - 83.5, - 82.5, - 82.5, - 83.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTGCTTCATCGGAAG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 68.5, - 68.5, - 67.5, - 67.5, - 68.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACCTTAGTCTCCATC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 54.5, - 54.5, - 53.5, - 53.5, - 54.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTTCGGCACAGGTTT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 37.5, - 37.5, - 36.5, - 36.5, - 37.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTCTCAAGTGTAATGA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 207.5, - 207.5, - 206.5, - 206.5, - 207.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAGGATCTGAAAGA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 205.5, - 205.5, - 204.5, - 204.5, - 205.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TAAACCGTCGTACCGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 204.5, - 204.5, - 203.5, - 203.5, - 204.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCATCTTCAACT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 193.5, - 193.5, - 192.5, - 192.5, - 193.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTACCGGTGGTCCGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 192.5, - 192.5, - 191.5, - 191.5, - 192.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGTCCTCACGATGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 188.5, - 188.5, - 187.5, - 187.5, - 188.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCTCCTGTTTCCACC-1
CTCTTTTCAGTTCA_r1
nan", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 187.5, - 187.5, - 186.5, - 186.5, - 187.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGCGATAGTAGGTGC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 157.5, - 157.5, - 156.5, - 156.5, - 157.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGCCGCATGCAATC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 156.5, - 156.5, - 155.5, - 155.5, - 156.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCAAACTTCACGCATA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 155.5, - 155.5, - 154.5, - 154.5, - 155.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAAGTTCAACCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 154.5, - 154.5, - 153.5, - 153.5, - 154.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTGGGGTTCACGGC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 152.5, - 152.5, - 151.5, - 151.5, - 152.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGGTATGAATG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 151.5, - 151.5, - 150.5, - 150.5, - 151.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTCACTAGTCCAGGA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 149.5, - 149.5, - 148.5, - 148.5, - 149.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCATTCGAGCGATGAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 126.5, - 126.5, - 125.5, - 125.5, - 126.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACTCATTCGGTTCGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 125.5, - 125.5, - 124.5, - 124.5, - 125.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGAATCCAAGGCTCC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 123.5, - 123.5, - 122.5, - 122.5, - 123.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCAGGCAATTA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 120.5, - 120.5, - 119.5, - 119.5, - 120.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAGGAGGAGTTGC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 119.5, - 119.5, - 118.5, - 118.5, - 119.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCACACAAGCCC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 116.5, - 116.5, - 115.5, - 115.5, - 116.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AACCGCGTCCACTGGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 88.5, - 88.5, - 87.5, - 87.5, - 88.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCCTCTAGTGCTGTAT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 85.5, - 85.5, - 84.5, - 84.5, - 85.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTCTGCATTCCTCG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 84.5, - 84.5, - 83.5, - 83.5, - 84.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCATGGGTCACAAGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 82.5, - 82.5, - 81.5, - 81.5, - 82.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCACACGTAGCGTAG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 65.5, - 65.5, - 64.5, - 64.5, - 65.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAGACCTCATATCGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 58.5, - 58.5, - 57.5, - 57.5, - 58.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCATAGTTACGCGC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 57.5, - 57.5, - 56.5, - 56.5, - 57.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCATTACAGGCATGGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 56.5, - 56.5, - 55.5, - 55.5, - 56.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTAGAGCCTCGTG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 55.5, - 55.5, - 54.5, - 54.5, - 55.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATTTGTGGCCCTA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 52.5, - 52.5, - 51.5, - 51.5, - 52.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAACTTTCCCATTAT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 50.5, - 50.5, - 49.5, - 49.5, - 50.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCACAAGTCCACTGGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 49.5, - 49.5, - 48.5, - 48.5, - 49.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGTTGGTCAGCGATCC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 20.5, - 20.5, - 19.5, - 19.5, - 20.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACCGTAATCCGAGCCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 19.5, - 19.5, - 18.5, - 18.5, - 19.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GAATAAGTCAACCAAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 18.5, - 18.5, - 17.5, - 17.5, - 18.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAGACCTCATCACCC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 206.5, - 206.5, - 205.5, - 205.5, - 206.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGGCGTCACGCCAGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 189.5, - 189.5, - 188.5, - 188.5, - 189.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACAGCTACAGCTGGCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 186.5, - 186.5, - 185.5, - 185.5, - 186.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTATCTAGGTGACCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 185.5, - 185.5, - 184.5, - 184.5, - 185.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGATGTCTACTTAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 184.5, - 184.5, - 183.5, - 183.5, - 184.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGAATAGGTCGCTTCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 183.5, - 183.5, - 182.5, - 182.5, - 183.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGAGTAGACCCACC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 182.5, - 182.5, - 181.5, - 181.5, - 182.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAACCTCAGGACGAAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 181.5, - 181.5, - 180.5, - 180.5, - 181.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAAACTCACGATGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 180.5, - 180.5, - 179.5, - 179.5, - 180.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACACCTCACGACTCG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 179.5, - 179.5, - 178.5, - 178.5, - 179.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAGGCGTTCACCTC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 178.5, - 178.5, - 177.5, - 177.5, - 178.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACCACTAGCCAGAAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 177.5, - 177.5, - 176.5, - 176.5, - 177.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATGACAAGGAGTTGC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 176.5, - 176.5, - 175.5, - 175.5, - 176.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCGTTCAAGGACGAAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 175.5, - 175.5, - 174.5, - 174.5, - 175.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTATTAAGTGAATTG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 174.5, - 174.5, - 173.5, - 173.5, - 174.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGCGGTAAGCCACGCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 173.5, - 173.5, - 172.5, - 172.5, - 173.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGCGTTTGTCGCGAAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 172.5, - 172.5, - 171.5, - 171.5, - 172.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTAGGCCATTCCTGC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 171.5, - 171.5, - 170.5, - 170.5, - 171.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTGTAAAGGCTAGGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 170.5, - 170.5, - 169.5, - 169.5, - 170.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAAACAAGGTTCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 169.5, - 169.5, - 168.5, - 168.5, - 169.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTCCATCGTAGATC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 168.5, - 168.5, - 167.5, - 167.5, - 168.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTATTGCAGCCTTTC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 167.5, - 167.5, - 166.5, - 166.5, - 167.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGCATATGCTG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 166.5, - 166.5, - 165.5, - 165.5, - 166.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACTTGTTCCAAATGC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 165.5, - 165.5, - 164.5, - 164.5, - 165.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TATTACCGTCTCTTAT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 164.5, - 164.5, - 163.5, - 163.5, - 164.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCATTACAGCTTTGGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 163.5, - 163.5, - 162.5, - 162.5, - 163.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTTCGGTCTTACCTA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 162.5, - 162.5, - 161.5, - 161.5, - 162.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGCATCAAGCGAGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 161.5, - 161.5, - 160.5, - 160.5, - 161.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTCCGTAAGTGTA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 160.5, - 160.5, - 159.5, - 159.5, - 160.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTCTCAATCCTTGCCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 159.5, - 159.5, - 158.5, - 158.5, - 159.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTTGCGCCAAACTGCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 158.5, - 158.5, - 157.5, - 157.5, - 158.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAACTAGACTAGAT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 153.5, - 153.5, - 152.5, - 152.5, - 153.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGCCAGGTAAATGTG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 117.5, - 117.5, - 116.5, - 116.5, - 117.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGCTCTCAGGTAAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 114.5, - 114.5, - 113.5, - 113.5, - 114.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGCCAATCCGAATGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 75.5, - 75.5, - 74.5, - 74.5, - 75.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTCGGGTCACCGTTGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 69.5, - 69.5, - 68.5, - 68.5, - 69.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCAGCGTGACTACT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 67.5, - 67.5, - 66.5, - 66.5, - 67.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGTCTGCGGCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 66.5, - 66.5, - 65.5, - 65.5, - 66.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCAATTCATCCCACT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 63.5, - 63.5, - 62.5, - 62.5, - 63.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGCGATTCAATCTCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 62.5, - 62.5, - 61.5, - 61.5, - 62.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGCTTCAGCGTGAGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 61.5, - 61.5, - 60.5, - 60.5, - 61.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGTCCCATCTCGATGA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 60.5, - 60.5, - 59.5, - 59.5, - 60.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACCTATTCCGTAGGC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 59.5, - 59.5, - 58.5, - 58.5, - 59.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AACTTTCTCGACGGAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 53.5, - 53.5, - 52.5, - 52.5, - 53.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGGACGTGTGAAATCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 51.5, - 51.5, - 50.5, - 50.5, - 51.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAAAGTCTCGTTC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 48.5, - 48.5, - 47.5, - 47.5, - 48.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAACACTTAAGC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 47.5, - 47.5, - 46.5, - 46.5, - 47.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATCGTATCCTTGCCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 46.5, - 46.5, - 45.5, - 45.5, - 46.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGACTTTGTATAGGGC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 45.5, - 45.5, - 44.5, - 44.5, - 45.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCGGTAGCAGACACTT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 44.5, - 44.5, - 43.5, - 43.5, - 44.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATGTCGAGATG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 43.5, - 43.5, - 42.5, - 42.5, - 43.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAAGACTCCTAGTGA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 41.5, - 41.5, - 40.5, - 40.5, - 41.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGGGTTCAGGACGTA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 40.5, - 40.5, - 39.5, - 39.5, - 40.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATTCAGAGACAGACC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 39.5, - 39.5, - 38.5, - 38.5, - 39.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAATCTGTGCGAAAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 17.5, - 17.5, - 16.5, - 16.5, - 17.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCACTCAAGGTAA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 8.5, - 8.5, - 7.5, - 7.5, - 8.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGCCGTCACCAGGTC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 7.5, - 7.5, - 6.5, - 6.5, - 7.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCCAAACAAACGTGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 195.5, - 195.5, - 194.5, - 194.5, - 195.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCTGCTGTATATCCG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 194.5, - 194.5, - 193.5, - 193.5, - 194.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATCGTACATAACCTG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 73.5, - 73.5, - 72.5, - 72.5, - 73.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGATCGGGTCTAACGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 70.5, - 70.5, - 69.5, - 69.5, - 70.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATCAAGGCTCC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 64.5, - 64.5, - 63.5, - 63.5, - 64.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTACTTTGTTAAGAAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 42.5, - 42.5, - 41.5, - 41.5, - 42.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGTGATCATGGTCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 38.5, - 38.5, - 37.5, - 37.5, - 38.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAAGAGGTCGATTGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 10.5, - 10.5, - 9.5, - 9.5, - 10.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATCAGGTCGGATCC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 9.5, - 9.5, - 8.5, - 8.5, - 9.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AAGGAGCTCTACCTGC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 6.5, - 6.5, - 5.5, - 5.5, - 6.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAGAGTAGCCCTAAT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 76.5, - 76.5, - 75.5, - 75.5, - 76.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACGGATGTCGCCATG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 74.5, - 74.5, - 73.5, - 73.5, - 74.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATCGGGAGACACGAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 72.5, - 72.5, - 71.5, - 71.5, - 72.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGACAACCATGCCCGA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 71.5, - 71.5, - 70.5, - 70.5, - 71.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GAGGTGACATCGATGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 14.5, - 14.5, - 13.5, - 13.5, - 14.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGAGGATCCAGATCA-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 13.5, - 13.5, - 12.5, - 12.5, - 13.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACTGTCCAGTATG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 12.5, - 12.5, - 11.5, - 11.5, - 12.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTCCGTAGCTCCG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 11.5, - 11.5, - 10.5, - 10.5, - 11.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCGTACGTCCGTCAG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 5.5, - 5.5, - 4.5, - 4.5, - 5.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCAGAAGTCGCATCG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 79.5, - 79.5, - 78.5, - 78.5, - 79.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTTCTGAGTTAAGTG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 15.5, - 15.5, - 14.5, - 14.5, - 15.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGGGTCCAGCTGCAC-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 3.5, - 3.5, - 2.5, - 2.5, - 3.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGCCAAAGCCTTGAT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 81.5, - 81.5, - 80.5, - 80.5, - 81.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAAACCATAAAGGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 80.5, - 80.5, - 79.5, - 79.5, - 80.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATTTGTTGCCTCT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 78.5, - 78.5, - 77.5, - 77.5, - 78.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATATTCCAGGGTTAG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 16.5, - 16.5, - 15.5, - 15.5, - 16.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b70c13", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTAGAAGAATGTGT-1
CTCTTTTCAGTTCA_r1
CGCCG[111:3D]AATGG", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 4.5, - 4.5, - 3.5, - 3.5, - 4.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATTCGCTCCCACTTG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 1.5, - 1.5, - 0.5, - 0.5, - 1.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b3541d", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAATGGTCACCAGGC-1
CTCTTTTCAGTTCA_r1
TACGC[109:3D]AAAAT", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 0.5, - 0.5, - -0.5, - -0.5, - 0.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGGAACTCGGCTTGG-1
CTCTTTTCAGTTCA_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 2.5, - 2.5, - 1.5, - 1.5, - 2.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGCCAGAGGCCATAG-1
CTCTTTTCAGTTCA_r1
nan", - "type": "scatter", - "x": [ - 4.800000000000001, - 4, - 4, - 4.800000000000001, - 4.800000000000001 - ], - "y": [ - 77.5, - 77.5, - 76.5, - 76.5, - 77.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGGAGACAAGGGTCA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 34.5, - 34.5, - 33.5, - 33.5, - 34.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCATTTCCAGGGCT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 30.5, - 30.5, - 29.5, - 29.5, - 30.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCCTAATCTCAAACG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 29.5, - 29.5, - 28.5, - 28.5, - 29.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATGACACATCATCCC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 28.5, - 28.5, - 27.5, - 27.5, - 28.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AATCCAGCACCAGTTA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 218.5, - 218.5, - 217.5, - 217.5, - 218.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATTCAGGTATCAGTC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 198.5, - 198.5, - 197.5, - 197.5, - 198.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGTAGGTCGAGAGCA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 197.5, - 197.5, - 196.5, - 196.5, - 197.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTACAGGTGTTTGTG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 107.5, - 107.5, - 106.5, - 106.5, - 107.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGAGCAACGGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 105.5, - 105.5, - 104.5, - 104.5, - 105.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGCCAGAGGTCATCT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 101.5, - 101.5, - 100.5, - 100.5, - 101.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TATCAGGTCACGATGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 100.5, - 100.5, - 99.5, - 99.5, - 100.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGACTTAGCTAAGAT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 98.5, - 98.5, - 97.5, - 97.5, - 98.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GAACGGAAGGCAGGTT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 97.5, - 97.5, - 96.5, - 96.5, - 97.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCTCTCGTTTA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 33.5, - 33.5, - 32.5, - 32.5, - 33.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AATCGGTTCAGGCAAG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 32.5, - 32.5, - 31.5, - 31.5, - 32.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGTAACAGCGATATA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 31.5, - 31.5, - 30.5, - 30.5, - 31.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GACACGCCAATAAGCA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 27.5, - 27.5, - 26.5, - 26.5, - 27.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGGGACATAGAAAC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 26.5, - 26.5, - 25.5, - 25.5, - 26.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCTGTTCGAGAGCA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 25.5, - 25.5, - 24.5, - 24.5, - 25.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACCAGTACAAGGACTG-1
CTCTTTTCAGTTCA_r2
nan", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 24.5, - 24.5, - 23.5, - 23.5, - 24.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCACGAAAGGATATAC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 224.5, - 224.5, - 223.5, - 223.5, - 224.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAACCTCAGACAGGCT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 221.5, - 221.5, - 220.5, - 220.5, - 221.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACGGGCTCGTCTGAA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 220.5, - 220.5, - 219.5, - 219.5, - 220.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATCAAGCATCCAACA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 217.5, - 217.5, - 216.5, - 216.5, - 217.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGAGGGCAATCGAAA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 209.5, - 209.5, - 208.5, - 208.5, - 209.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGAAGCACACGCTG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 208.5, - 208.5, - 207.5, - 207.5, - 208.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTTGCGCTCCGTCAAA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 199.5, - 199.5, - 198.5, - 198.5, - 199.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTAGAAGCAGATCG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 196.5, - 196.5, - 195.5, - 195.5, - 196.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAACAGGTGCCT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 145.5, - 145.5, - 144.5, - 144.5, - 145.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AAAGATGGTAGCGTGA-1
CTCTTTTCAGTTCA_r2
nan", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 143.5, - 143.5, - 142.5, - 142.5, - 143.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCACAAGAGTGAACGC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 141.5, - 141.5, - 140.5, - 140.5, - 141.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCGTACTTCTT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 108.5, - 108.5, - 107.5, - 107.5, - 108.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGATGTTAGAACTGTA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 106.5, - 106.5, - 105.5, - 105.5, - 106.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTCTGACAAACGTGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 104.5, - 104.5, - 103.5, - 103.5, - 104.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCTAATCAGGCCCA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 99.5, - 99.5, - 98.5, - 98.5, - 99.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GACGCGTGTGCAGTAG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 95.5, - 95.5, - 94.5, - 94.5, - 95.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTCATTTTCCTCAACC-1
CTCTTTTCAGTTCA_r2
nan", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 22.5, - 22.5, - 21.5, - 21.5, - 22.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCGCGAGTCAGCTCGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 225.5, - 225.5, - 224.5, - 224.5, - 225.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTCCATCTGTTTGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 219.5, - 219.5, - 218.5, - 218.5, - 219.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCTCCTAGTGTACGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 212.5, - 212.5, - 211.5, - 211.5, - 212.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTCACTTCATCATTC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 211.5, - 211.5, - 210.5, - 210.5, - 211.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAGGTAAGGCATTGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 210.5, - 210.5, - 209.5, - 209.5, - 210.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCATTCGAGTCATGCT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 200.5, - 200.5, - 199.5, - 199.5, - 200.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGGGAGCAAAGGAAG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 147.5, - 147.5, - 146.5, - 146.5, - 147.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATATTCGTCGAATCT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 146.5, - 146.5, - 145.5, - 145.5, - 146.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCAGAGGACGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 142.5, - 142.5, - 141.5, - 141.5, - 142.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCCTAAGCTGCGAA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 140.5, - 140.5, - 139.5, - 139.5, - 140.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTGAAACCATTTGCCC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 131.5, - 131.5, - 130.5, - 130.5, - 131.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGAGCTTTCAAGATCC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 130.5, - 130.5, - 129.5, - 129.5, - 130.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCACCTAGTACACCT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 129.5, - 129.5, - 128.5, - 128.5, - 129.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCAATTTCACCTCGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 128.5, - 128.5, - 127.5, - 127.5, - 128.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTCAGTTCGAGAGCA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 124.5, - 124.5, - 123.5, - 123.5, - 124.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTGGTGAGGCCGAAT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 121.5, - 121.5, - 120.5, - 120.5, - 121.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATTATCCAGGCGATA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 113.5, - 113.5, - 112.5, - 112.5, - 113.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGTGTGCACGGTGTC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 112.5, - 112.5, - 111.5, - 111.5, - 112.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGACCAAGGAATTAC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 111.5, - 111.5, - 110.5, - 110.5, - 111.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGTGAGGTCGGTCTAA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 110.5, - 110.5, - 109.5, - 109.5, - 110.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTACATTGTTCGCGAC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 109.5, - 109.5, - 108.5, - 108.5, - 109.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGTGACAGGGTTAG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 103.5, - 103.5, - 102.5, - 102.5, - 103.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCTGGTAGGTGCTTT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 102.5, - 102.5, - 101.5, - 101.5, - 102.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATCGAAGTTTGTTTC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 96.5, - 96.5, - 95.5, - 95.5, - 96.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATAGTCTACTTAC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 94.5, - 94.5, - 93.5, - 93.5, - 94.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGCAGCGTGTGCGTC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 36.5, - 36.5, - 35.5, - 35.5, - 36.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCAATCCTCCTTTACA-1
CTCTTTTCAGTTCA_r2
nan", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 35.5, - 35.5, - 34.5, - 34.5, - 35.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACAGCCGAGGTAGCCA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 23.5, - 23.5, - 22.5, - 22.5, - 23.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTATTATCCGCATCT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 21.5, - 21.5, - 20.5, - 20.5, - 21.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGGGAGTCAGCGACC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 223.5, - 223.5, - 222.5, - 222.5, - 223.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCCACCCAGCCAGAA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 222.5, - 222.5, - 221.5, - 221.5, - 222.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AAACCTGTCCTTAATC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 216.5, - 216.5, - 215.5, - 215.5, - 216.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTGGACCACAGAGGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 215.5, - 215.5, - 214.5, - 214.5, - 215.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACGTTCATATCGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 214.5, - 214.5, - 213.5, - 213.5, - 214.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTACCGTCTACTATC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 213.5, - 213.5, - 212.5, - 212.5, - 213.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCGAGGGTACATCCA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 203.5, - 203.5, - 202.5, - 202.5, - 203.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACGTCACTAGTAC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 202.5, - 202.5, - 201.5, - 201.5, - 202.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGAGTGGTCCC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 201.5, - 201.5, - 200.5, - 200.5, - 201.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATGTCACCTAA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 191.5, - 191.5, - 190.5, - 190.5, - 191.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCAAGCTGCAAG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 190.5, - 190.5, - 189.5, - 189.5, - 190.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGATGTTTCCGGCACA-1
CTCTTTTCAGTTCA_r2
nan", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 150.5, - 150.5, - 149.5, - 149.5, - 150.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGGAGATGTCAATACC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 148.5, - 148.5, - 147.5, - 147.5, - 148.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCTACTAGCTCTCGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 144.5, - 144.5, - 143.5, - 143.5, - 144.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGAGTCGATAA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 139.5, - 139.5, - 138.5, - 138.5, - 139.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATCTGAGGCTACGA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 138.5, - 138.5, - 137.5, - 137.5, - 138.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAGGAGTGCACTTA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 137.5, - 137.5, - 136.5, - 136.5, - 137.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCTGTGTATGAAAC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 136.5, - 136.5, - 135.5, - 135.5, - 136.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGACATTAGGAGTCTG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 135.5, - 135.5, - 134.5, - 134.5, - 135.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGGAATGAGCCACCTG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 134.5, - 134.5, - 133.5, - 133.5, - 134.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCCGATCACCGAATT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 133.5, - 133.5, - 132.5, - 132.5, - 133.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTATTGGTGAGTATA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 132.5, - 132.5, - 131.5, - 131.5, - 132.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATGGTAGGACCACA-1
CTCTTTTCAGTTCA_r2
nan", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 127.5, - 127.5, - 126.5, - 126.5, - 127.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAGCAACACCATGTA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 122.5, - 122.5, - 121.5, - 121.5, - 122.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACACCAATCCACTGGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 118.5, - 118.5, - 117.5, - 117.5, - 118.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTGTGCTCAACTGGCC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 115.5, - 115.5, - 114.5, - 114.5, - 115.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTAGTTCAGTGACATA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 93.5, - 93.5, - 92.5, - 92.5, - 93.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCACTCTTCGAGAGCA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 92.5, - 92.5, - 91.5, - 91.5, - 92.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATACGGTTCCCGAG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 91.5, - 91.5, - 90.5, - 90.5, - 91.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTTTCTTCACAACGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 90.5, - 90.5, - 89.5, - 89.5, - 90.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTTCTAGTACTTCTT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 89.5, - 89.5, - 88.5, - 88.5, - 89.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGTGGTAGTGTTGAGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 87.5, - 87.5, - 86.5, - 86.5, - 87.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTCTAAGTTTGGGCC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 86.5, - 86.5, - 85.5, - 85.5, - 86.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGAATCGTTATCGGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 83.5, - 83.5, - 82.5, - 82.5, - 83.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTGCTTCATCGGAAG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 68.5, - 68.5, - 67.5, - 67.5, - 68.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACCTTAGTCTCCATC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 54.5, - 54.5, - 53.5, - 53.5, - 54.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTTCGGCACAGGTTT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 37.5, - 37.5, - 36.5, - 36.5, - 37.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTCTCAAGTGTAATGA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 207.5, - 207.5, - 206.5, - 206.5, - 207.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAGGATCTGAAAGA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 205.5, - 205.5, - 204.5, - 204.5, - 205.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TAAACCGTCGTACCGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 204.5, - 204.5, - 203.5, - 203.5, - 204.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCATCTTCAACT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 193.5, - 193.5, - 192.5, - 192.5, - 193.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTACCGGTGGTCCGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 192.5, - 192.5, - 191.5, - 191.5, - 192.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGTCCTCACGATGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 188.5, - 188.5, - 187.5, - 187.5, - 188.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCTCCTGTTTCCACC-1
CTCTTTTCAGTTCA_r2
nan", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 187.5, - 187.5, - 186.5, - 186.5, - 187.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGCGATAGTAGGTGC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 157.5, - 157.5, - 156.5, - 156.5, - 157.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGCCGCATGCAATC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 156.5, - 156.5, - 155.5, - 155.5, - 156.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCAAACTTCACGCATA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 155.5, - 155.5, - 154.5, - 154.5, - 155.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAAGTTCAACCA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 154.5, - 154.5, - 153.5, - 153.5, - 154.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTGGGGTTCACGGC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 152.5, - 152.5, - 151.5, - 151.5, - 152.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGGTATGAATG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 151.5, - 151.5, - 150.5, - 150.5, - 151.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTCACTAGTCCAGGA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 149.5, - 149.5, - 148.5, - 148.5, - 149.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCATTCGAGCGATGAC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 126.5, - 126.5, - 125.5, - 125.5, - 126.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACTCATTCGGTTCGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 125.5, - 125.5, - 124.5, - 124.5, - 125.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGAATCCAAGGCTCC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 123.5, - 123.5, - 122.5, - 122.5, - 123.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCAGGCAATTA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 120.5, - 120.5, - 119.5, - 119.5, - 120.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAGGAGGAGTTGC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 119.5, - 119.5, - 118.5, - 118.5, - 119.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCACACAAGCCC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 116.5, - 116.5, - 115.5, - 115.5, - 116.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AACCGCGTCCACTGGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 88.5, - 88.5, - 87.5, - 87.5, - 88.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCCTCTAGTGCTGTAT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 85.5, - 85.5, - 84.5, - 84.5, - 85.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTCTGCATTCCTCG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 84.5, - 84.5, - 83.5, - 83.5, - 84.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCATGGGTCACAAGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 82.5, - 82.5, - 81.5, - 81.5, - 82.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCACACGTAGCGTAG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 65.5, - 65.5, - 64.5, - 64.5, - 65.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAGACCTCATATCGG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 58.5, - 58.5, - 57.5, - 57.5, - 58.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCATAGTTACGCGC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 57.5, - 57.5, - 56.5, - 56.5, - 57.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCATTACAGGCATGGT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 56.5, - 56.5, - 55.5, - 55.5, - 56.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTAGAGCCTCGTG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 55.5, - 55.5, - 54.5, - 54.5, - 55.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATTTGTGGCCCTA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 52.5, - 52.5, - 51.5, - 51.5, - 52.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAACTTTCCCATTAT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 50.5, - 50.5, - 49.5, - 49.5, - 50.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCACAAGTCCACTGGG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 49.5, - 49.5, - 48.5, - 48.5, - 49.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGTTGGTCAGCGATCC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 20.5, - 20.5, - 19.5, - 19.5, - 20.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACCGTAATCCGAGCCA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 19.5, - 19.5, - 18.5, - 18.5, - 19.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GAATAAGTCAACCAAC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 18.5, - 18.5, - 17.5, - 17.5, - 18.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAGACCTCATCACCC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 206.5, - 206.5, - 205.5, - 205.5, - 206.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGGCGTCACGCCAGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 189.5, - 189.5, - 188.5, - 188.5, - 189.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACAGCTACAGCTGGCT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 186.5, - 186.5, - 185.5, - 185.5, - 186.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTATCTAGGTGACCA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 185.5, - 185.5, - 184.5, - 184.5, - 185.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGATGTCTACTTAC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 184.5, - 184.5, - 183.5, - 183.5, - 184.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGAATAGGTCGCTTCT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 183.5, - 183.5, - 182.5, - 182.5, - 183.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGAGTAGACCCACC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 182.5, - 182.5, - 181.5, - 181.5, - 182.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAACCTCAGGACGAAA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 181.5, - 181.5, - 180.5, - 180.5, - 181.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAAACTCACGATGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 180.5, - 180.5, - 179.5, - 179.5, - 180.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACACCTCACGACTCG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 179.5, - 179.5, - 178.5, - 178.5, - 179.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAGGCGTTCACCTC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 178.5, - 178.5, - 177.5, - 177.5, - 178.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACCACTAGCCAGAAC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 177.5, - 177.5, - 176.5, - 176.5, - 177.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATGACAAGGAGTTGC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 176.5, - 176.5, - 175.5, - 175.5, - 176.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCGTTCAAGGACGAAA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 175.5, - 175.5, - 174.5, - 174.5, - 175.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTATTAAGTGAATTG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 174.5, - 174.5, - 173.5, - 173.5, - 174.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGCGGTAAGCCACGCT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 173.5, - 173.5, - 172.5, - 172.5, - 173.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGCGTTTGTCGCGAAA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 172.5, - 172.5, - 171.5, - 171.5, - 172.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTAGGCCATTCCTGC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 171.5, - 171.5, - 170.5, - 170.5, - 171.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTGTAAAGGCTAGGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 170.5, - 170.5, - 169.5, - 169.5, - 170.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAAACAAGGTTCT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 169.5, - 169.5, - 168.5, - 168.5, - 169.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTCCATCGTAGATC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 168.5, - 168.5, - 167.5, - 167.5, - 168.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTATTGCAGCCTTTC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 167.5, - 167.5, - 166.5, - 166.5, - 167.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGCATATGCTG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 166.5, - 166.5, - 165.5, - 165.5, - 166.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACTTGTTCCAAATGC-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 165.5, - 165.5, - 164.5, - 164.5, - 165.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TATTACCGTCTCTTAT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 164.5, - 164.5, - 163.5, - 163.5, - 164.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCATTACAGCTTTGGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 163.5, - 163.5, - 162.5, - 162.5, - 163.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTTCGGTCTTACCTA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 162.5, - 162.5, - 161.5, - 161.5, - 162.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGCATCAAGCGAGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 161.5, - 161.5, - 160.5, - 160.5, - 161.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTCCGTAAGTGTA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 160.5, - 160.5, - 159.5, - 159.5, - 160.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTCTCAATCCTTGCCA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 159.5, - 159.5, - 158.5, - 158.5, - 159.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTTGCGCCAAACTGCT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 158.5, - 158.5, - 157.5, - 157.5, - 158.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAACTAGACTAGAT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 153.5, - 153.5, - 152.5, - 152.5, - 153.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGCCAGGTAAATGTG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 117.5, - 117.5, - 116.5, - 116.5, - 117.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGCTCTCAGGTAAA-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 114.5, - 114.5, - 113.5, - 113.5, - 114.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGCCAATCCGAATGT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 75.5, - 75.5, - 74.5, - 74.5, - 75.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTCGGGTCACCGTTGG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 69.5, - 69.5, - 68.5, - 68.5, - 69.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCAGCGTGACTACT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 67.5, - 67.5, - 66.5, - 66.5, - 67.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGTCTGCGGCA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 66.5, - 66.5, - 65.5, - 65.5, - 66.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCAATTCATCCCACT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 63.5, - 63.5, - 62.5, - 62.5, - 63.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGCGATTCAATCTCT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 62.5, - 62.5, - 61.5, - 61.5, - 62.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGCTTCAGCGTGAGT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 61.5, - 61.5, - 60.5, - 60.5, - 61.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGTCCCATCTCGATGA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 60.5, - 60.5, - 59.5, - 59.5, - 60.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACCTATTCCGTAGGC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 59.5, - 59.5, - 58.5, - 58.5, - 59.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AACTTTCTCGACGGAA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 53.5, - 53.5, - 52.5, - 52.5, - 53.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGGACGTGTGAAATCA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 51.5, - 51.5, - 50.5, - 50.5, - 51.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAAAGTCTCGTTC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 48.5, - 48.5, - 47.5, - 47.5, - 48.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAACACTTAAGC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 47.5, - 47.5, - 46.5, - 46.5, - 47.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATCGTATCCTTGCCA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 46.5, - 46.5, - 45.5, - 45.5, - 46.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGACTTTGTATAGGGC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 45.5, - 45.5, - 44.5, - 44.5, - 45.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCGGTAGCAGACACTT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 44.5, - 44.5, - 43.5, - 43.5, - 44.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATGTCGAGATG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 43.5, - 43.5, - 42.5, - 42.5, - 43.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAAGACTCCTAGTGA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 41.5, - 41.5, - 40.5, - 40.5, - 41.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGGGTTCAGGACGTA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 40.5, - 40.5, - 39.5, - 39.5, - 40.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATTCAGAGACAGACC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 39.5, - 39.5, - 38.5, - 38.5, - 39.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAATCTGTGCGAAAC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 17.5, - 17.5, - 16.5, - 16.5, - 17.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCACTCAAGGTAA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 8.5, - 8.5, - 7.5, - 7.5, - 8.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGCCGTCACCAGGTC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 7.5, - 7.5, - 6.5, - 6.5, - 7.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCCAAACAAACGTGG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 195.5, - 195.5, - 194.5, - 194.5, - 195.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCTGCTGTATATCCG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 194.5, - 194.5, - 193.5, - 193.5, - 194.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATCGTACATAACCTG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 73.5, - 73.5, - 72.5, - 72.5, - 73.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGATCGGGTCTAACGT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 70.5, - 70.5, - 69.5, - 69.5, - 70.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATCAAGGCTCC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 64.5, - 64.5, - 63.5, - 63.5, - 64.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTACTTTGTTAAGAAC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 42.5, - 42.5, - 41.5, - 41.5, - 42.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGTGATCATGGTCA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 38.5, - 38.5, - 37.5, - 37.5, - 38.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAAGAGGTCGATTGT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 10.5, - 10.5, - 9.5, - 9.5, - 10.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATCAGGTCGGATCC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 9.5, - 9.5, - 8.5, - 8.5, - 9.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AAGGAGCTCTACCTGC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 6.5, - 6.5, - 5.5, - 5.5, - 6.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAGAGTAGCCCTAAT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 76.5, - 76.5, - 75.5, - 75.5, - 76.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACGGATGTCGCCATG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 74.5, - 74.5, - 73.5, - 73.5, - 74.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATCGGGAGACACGAC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 72.5, - 72.5, - 71.5, - 71.5, - 72.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGACAACCATGCCCGA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 71.5, - 71.5, - 70.5, - 70.5, - 71.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GAGGTGACATCGATGT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 14.5, - 14.5, - 13.5, - 13.5, - 14.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGAGGATCCAGATCA-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 13.5, - 13.5, - 12.5, - 12.5, - 13.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACTGTCCAGTATG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 12.5, - 12.5, - 11.5, - 11.5, - 12.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTCCGTAGCTCCG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 11.5, - 11.5, - 10.5, - 10.5, - 11.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCGTACGTCCGTCAG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 5.5, - 5.5, - 4.5, - 4.5, - 5.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCAGAAGTCGCATCG-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 79.5, - 79.5, - 78.5, - 78.5, - 79.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTTCTGAGTTAAGTG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 15.5, - 15.5, - 14.5, - 14.5, - 15.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGGGTCCAGCTGCAC-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 3.5, - 3.5, - 2.5, - 2.5, - 3.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGCCAAAGCCTTGAT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 81.5, - 81.5, - 80.5, - 80.5, - 81.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAAACCATAAAGGT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 80.5, - 80.5, - 79.5, - 79.5, - 80.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATTTGTTGCCTCT-1
CTCTTTTCAGTTCA_r2
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 78.5, - 78.5, - 77.5, - 77.5, - 78.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATATTCCAGGGTTAG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 16.5, - 16.5, - 15.5, - 15.5, - 16.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#a14c7b", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTAGAAGAATGTGT-1
CTCTTTTCAGTTCA_r2
CGATA[166:1I]TTCTCT", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 4.5, - 4.5, - 3.5, - 3.5, - 4.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#73e1de", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATTCGCTCCCACTTG-1
CTCTTTTCAGTTCA_r2
CATAA[161:6D]CTCTG", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 1.5, - 1.5, - 0.5, - 0.5, - 1.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#04b4e9", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAATGGTCACCAGGC-1
CTCTTTTCAGTTCA_r2
AACGA[164:4D]TCTGG", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 0.5, - 0.5, - -0.5, - -0.5, - 0.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f28574", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGGAACTCGGCTTGG-1
CTCTTTTCAGTTCA_r2
CGATA[166:1D]CTCTG", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 2.5, - 2.5, - 1.5, - 1.5, - 2.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGCCAGAGGCCATAG-1
CTCTTTTCAGTTCA_r2
nan", - "type": "scatter", - "x": [ - 6.000000000000001, - 5.2, - 5.2, - 6.000000000000001, - 6.000000000000001 - ], - "y": [ - 77.5, - 77.5, - 76.5, - 76.5, - 77.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#728cf3", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGGAGACAAGGGTCA-1
CTCTTTTCAGTTCA_r3
CTATT[215:6D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 34.5, - 34.5, - 33.5, - 33.5, - 34.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#728cf3", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCATTTCCAGGGCT-1
CTCTTTTCAGTTCA_r3
CTATT[215:6D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 30.5, - 30.5, - 29.5, - 29.5, - 30.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#728cf3", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCCTAATCTCAAACG-1
CTCTTTTCAGTTCA_r3
CTATT[215:6D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 29.5, - 29.5, - 28.5, - 28.5, - 29.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#728cf3", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATGACACATCATCCC-1
CTCTTTTCAGTTCA_r3
CTATT[215:6D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 28.5, - 28.5, - 27.5, - 27.5, - 28.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AATCCAGCACCAGTTA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 218.5, - 218.5, - 217.5, - 217.5, - 218.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATTCAGGTATCAGTC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 198.5, - 198.5, - 197.5, - 197.5, - 198.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGTAGGTCGAGAGCA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 197.5, - 197.5, - 196.5, - 196.5, - 197.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTACAGGTGTTTGTG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 107.5, - 107.5, - 106.5, - 106.5, - 107.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGAGCAACGGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 105.5, - 105.5, - 104.5, - 104.5, - 105.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGCCAGAGGTCATCT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 101.5, - 101.5, - 100.5, - 100.5, - 101.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TATCAGGTCACGATGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 100.5, - 100.5, - 99.5, - 99.5, - 100.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGACTTAGCTAAGAT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 98.5, - 98.5, - 97.5, - 97.5, - 98.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GAACGGAAGGCAGGTT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 97.5, - 97.5, - 96.5, - 96.5, - 97.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#728cf3", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCTCTCGTTTA-1
CTCTTTTCAGTTCA_r3
CTATT[215:6D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 33.5, - 33.5, - 32.5, - 32.5, - 33.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#728cf3", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AATCGGTTCAGGCAAG-1
CTCTTTTCAGTTCA_r3
CTATT[215:6D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 32.5, - 32.5, - 31.5, - 31.5, - 32.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#728cf3", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGTAACAGCGATATA-1
CTCTTTTCAGTTCA_r3
CTATT[215:6D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 31.5, - 31.5, - 30.5, - 30.5, - 31.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#fd9d76", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GACACGCCAATAAGCA-1
CTCTTTTCAGTTCA_r3
AATTC[220:1I]GGCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 27.5, - 27.5, - 26.5, - 26.5, - 27.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGGGACATAGAAAC-1
CTCTTTTCAGTTCA_r3
TAATT[219:2D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 26.5, - 26.5, - 25.5, - 25.5, - 26.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCTGTTCGAGAGCA-1
CTCTTTTCAGTTCA_r3
TAATT[219:2D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 25.5, - 25.5, - 24.5, - 24.5, - 25.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACCAGTACAAGGACTG-1
CTCTTTTCAGTTCA_r3
nan", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 24.5, - 24.5, - 23.5, - 23.5, - 24.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCACGAAAGGATATAC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 224.5, - 224.5, - 223.5, - 223.5, - 224.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAACCTCAGACAGGCT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 221.5, - 221.5, - 220.5, - 220.5, - 221.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACGGGCTCGTCTGAA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 220.5, - 220.5, - 219.5, - 219.5, - 220.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATCAAGCATCCAACA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 217.5, - 217.5, - 216.5, - 216.5, - 217.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGAGGGCAATCGAAA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 209.5, - 209.5, - 208.5, - 208.5, - 209.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGAAGCACACGCTG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 208.5, - 208.5, - 207.5, - 207.5, - 208.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTTGCGCTCCGTCAAA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 199.5, - 199.5, - 198.5, - 198.5, - 199.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTAGAAGCAGATCG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 196.5, - 196.5, - 195.5, - 195.5, - 196.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAACAGGTGCCT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 145.5, - 145.5, - 144.5, - 144.5, - 145.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AAAGATGGTAGCGTGA-1
CTCTTTTCAGTTCA_r3
nan", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 143.5, - 143.5, - 142.5, - 142.5, - 143.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCACAAGAGTGAACGC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 141.5, - 141.5, - 140.5, - 140.5, - 141.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCGTACTTCTT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 108.5, - 108.5, - 107.5, - 107.5, - 108.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGATGTTAGAACTGTA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 106.5, - 106.5, - 105.5, - 105.5, - 106.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTCTGACAAACGTGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 104.5, - 104.5, - 103.5, - 103.5, - 104.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCTAATCAGGCCCA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 99.5, - 99.5, - 98.5, - 98.5, - 99.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GACGCGTGTGCAGTAG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 95.5, - 95.5, - 94.5, - 94.5, - 95.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTCATTTTCCTCAACC-1
CTCTTTTCAGTTCA_r3
nan", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 22.5, - 22.5, - 21.5, - 21.5, - 22.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCGCGAGTCAGCTCGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 225.5, - 225.5, - 224.5, - 224.5, - 225.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTCCATCTGTTTGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 219.5, - 219.5, - 218.5, - 218.5, - 219.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCTCCTAGTGTACGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 212.5, - 212.5, - 211.5, - 211.5, - 212.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTCACTTCATCATTC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 211.5, - 211.5, - 210.5, - 210.5, - 211.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAGGTAAGGCATTGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 210.5, - 210.5, - 209.5, - 209.5, - 210.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCATTCGAGTCATGCT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 200.5, - 200.5, - 199.5, - 199.5, - 200.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGGGAGCAAAGGAAG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 147.5, - 147.5, - 146.5, - 146.5, - 147.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATATTCGTCGAATCT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 146.5, - 146.5, - 145.5, - 145.5, - 146.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCAGAGGACGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 142.5, - 142.5, - 141.5, - 141.5, - 142.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCCTAAGCTGCGAA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 140.5, - 140.5, - 139.5, - 139.5, - 140.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTGAAACCATTTGCCC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 131.5, - 131.5, - 130.5, - 130.5, - 131.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGAGCTTTCAAGATCC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 130.5, - 130.5, - 129.5, - 129.5, - 130.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCACCTAGTACACCT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 129.5, - 129.5, - 128.5, - 128.5, - 129.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCAATTTCACCTCGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 128.5, - 128.5, - 127.5, - 127.5, - 128.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTCAGTTCGAGAGCA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 124.5, - 124.5, - 123.5, - 123.5, - 124.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTGGTGAGGCCGAAT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 121.5, - 121.5, - 120.5, - 120.5, - 121.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATTATCCAGGCGATA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 113.5, - 113.5, - 112.5, - 112.5, - 113.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGTGTGCACGGTGTC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 112.5, - 112.5, - 111.5, - 111.5, - 112.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGACCAAGGAATTAC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 111.5, - 111.5, - 110.5, - 110.5, - 111.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGTGAGGTCGGTCTAA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 110.5, - 110.5, - 109.5, - 109.5, - 110.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTACATTGTTCGCGAC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 109.5, - 109.5, - 108.5, - 108.5, - 109.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGTGACAGGGTTAG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 103.5, - 103.5, - 102.5, - 102.5, - 103.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCTGGTAGGTGCTTT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 102.5, - 102.5, - 101.5, - 101.5, - 102.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATCGAAGTTTGTTTC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 96.5, - 96.5, - 95.5, - 95.5, - 96.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATAGTCTACTTAC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 94.5, - 94.5, - 93.5, - 93.5, - 94.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#8047b5", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGCAGCGTGTGCGTC-1
CTCTTTTCAGTTCA_r3
AATTC[220:1D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 36.5, - 36.5, - 35.5, - 35.5, - 36.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCAATCCTCCTTTACA-1
CTCTTTTCAGTTCA_r3
nan", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 35.5, - 35.5, - 34.5, - 34.5, - 35.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#8047b5", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACAGCCGAGGTAGCCA-1
CTCTTTTCAGTTCA_r3
AATTC[220:1D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 23.5, - 23.5, - 22.5, - 22.5, - 23.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#fd9d76", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTATTATCCGCATCT-1
CTCTTTTCAGTTCA_r3
AATTC[220:1I]GGCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 21.5, - 21.5, - 20.5, - 20.5, - 21.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGGGAGTCAGCGACC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 223.5, - 223.5, - 222.5, - 222.5, - 223.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCCACCCAGCCAGAA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 222.5, - 222.5, - 221.5, - 221.5, - 222.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AAACCTGTCCTTAATC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 216.5, - 216.5, - 215.5, - 215.5, - 216.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTGGACCACAGAGGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 215.5, - 215.5, - 214.5, - 214.5, - 215.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACGTTCATATCGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 214.5, - 214.5, - 213.5, - 213.5, - 214.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTACCGTCTACTATC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 213.5, - 213.5, - 212.5, - 212.5, - 213.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCGAGGGTACATCCA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 203.5, - 203.5, - 202.5, - 202.5, - 203.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACGTCACTAGTAC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 202.5, - 202.5, - 201.5, - 201.5, - 202.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGAGTGGTCCC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 201.5, - 201.5, - 200.5, - 200.5, - 201.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATGTCACCTAA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 191.5, - 191.5, - 190.5, - 190.5, - 191.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCAAGCTGCAAG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 190.5, - 190.5, - 189.5, - 189.5, - 190.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGATGTTTCCGGCACA-1
CTCTTTTCAGTTCA_r3
nan", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 150.5, - 150.5, - 149.5, - 149.5, - 150.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGGAGATGTCAATACC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 148.5, - 148.5, - 147.5, - 147.5, - 148.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCTACTAGCTCTCGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 144.5, - 144.5, - 143.5, - 143.5, - 144.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGAGTCGATAA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 139.5, - 139.5, - 138.5, - 138.5, - 139.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATCTGAGGCTACGA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 138.5, - 138.5, - 137.5, - 137.5, - 138.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAGGAGTGCACTTA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 137.5, - 137.5, - 136.5, - 136.5, - 137.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCTGTGTATGAAAC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 136.5, - 136.5, - 135.5, - 135.5, - 136.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGACATTAGGAGTCTG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 135.5, - 135.5, - 134.5, - 134.5, - 135.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGGAATGAGCCACCTG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 134.5, - 134.5, - 133.5, - 133.5, - 134.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCCGATCACCGAATT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 133.5, - 133.5, - 132.5, - 132.5, - 133.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTATTGGTGAGTATA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 132.5, - 132.5, - 131.5, - 131.5, - 132.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATGGTAGGACCACA-1
CTCTTTTCAGTTCA_r3
nan", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 127.5, - 127.5, - 126.5, - 126.5, - 127.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAGCAACACCATGTA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 122.5, - 122.5, - 121.5, - 121.5, - 122.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACACCAATCCACTGGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 118.5, - 118.5, - 117.5, - 117.5, - 118.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTGTGCTCAACTGGCC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 115.5, - 115.5, - 114.5, - 114.5, - 115.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTAGTTCAGTGACATA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 93.5, - 93.5, - 92.5, - 92.5, - 93.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCACTCTTCGAGAGCA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 92.5, - 92.5, - 91.5, - 91.5, - 92.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATACGGTTCCCGAG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 91.5, - 91.5, - 90.5, - 90.5, - 91.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTTTCTTCACAACGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 90.5, - 90.5, - 89.5, - 89.5, - 90.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTTCTAGTACTTCTT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 89.5, - 89.5, - 88.5, - 88.5, - 89.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGTGGTAGTGTTGAGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 87.5, - 87.5, - 86.5, - 86.5, - 87.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTCTAAGTTTGGGCC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 86.5, - 86.5, - 85.5, - 85.5, - 86.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGAATCGTTATCGGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 83.5, - 83.5, - 82.5, - 82.5, - 83.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#fd9d76", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTGCTTCATCGGAAG-1
CTCTTTTCAGTTCA_r3
AATTC[220:1I]GGCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 68.5, - 68.5, - 67.5, - 67.5, - 68.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACCTTAGTCTCCATC-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 54.5, - 54.5, - 53.5, - 53.5, - 54.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTTCGGCACAGGTTT-1
CTCTTTTCAGTTCA_r3
TAATT[219:2D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 37.5, - 37.5, - 36.5, - 36.5, - 37.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTCTCAAGTGTAATGA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 207.5, - 207.5, - 206.5, - 206.5, - 207.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAGGATCTGAAAGA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 205.5, - 205.5, - 204.5, - 204.5, - 205.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TAAACCGTCGTACCGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 204.5, - 204.5, - 203.5, - 203.5, - 204.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCATCTTCAACT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 193.5, - 193.5, - 192.5, - 192.5, - 193.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTACCGGTGGTCCGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 192.5, - 192.5, - 191.5, - 191.5, - 192.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGTCCTCACGATGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 188.5, - 188.5, - 187.5, - 187.5, - 188.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCTCCTGTTTCCACC-1
CTCTTTTCAGTTCA_r3
nan", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 187.5, - 187.5, - 186.5, - 186.5, - 187.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGCGATAGTAGGTGC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 157.5, - 157.5, - 156.5, - 156.5, - 157.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGCCGCATGCAATC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 156.5, - 156.5, - 155.5, - 155.5, - 156.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCAAACTTCACGCATA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 155.5, - 155.5, - 154.5, - 154.5, - 155.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAAGTTCAACCA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 154.5, - 154.5, - 153.5, - 153.5, - 154.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTGGGGTTCACGGC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 152.5, - 152.5, - 151.5, - 151.5, - 152.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGGTATGAATG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 151.5, - 151.5, - 150.5, - 150.5, - 151.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTCACTAGTCCAGGA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 149.5, - 149.5, - 148.5, - 148.5, - 149.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCATTCGAGCGATGAC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 126.5, - 126.5, - 125.5, - 125.5, - 126.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACTCATTCGGTTCGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 125.5, - 125.5, - 124.5, - 124.5, - 125.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGAATCCAAGGCTCC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 123.5, - 123.5, - 122.5, - 122.5, - 123.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCAGGCAATTA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 120.5, - 120.5, - 119.5, - 119.5, - 120.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAGGAGGAGTTGC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 119.5, - 119.5, - 118.5, - 118.5, - 119.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCACACAAGCCC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 116.5, - 116.5, - 115.5, - 115.5, - 116.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AACCGCGTCCACTGGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 88.5, - 88.5, - 87.5, - 87.5, - 88.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCCTCTAGTGCTGTAT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 85.5, - 85.5, - 84.5, - 84.5, - 85.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTCTGCATTCCTCG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 84.5, - 84.5, - 83.5, - 83.5, - 84.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCATGGGTCACAAGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 82.5, - 82.5, - 81.5, - 81.5, - 82.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9ded21", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCACACGTAGCGTAG-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]ACGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 65.5, - 65.5, - 64.5, - 64.5, - 65.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAGACCTCATATCGG-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 58.5, - 58.5, - 57.5, - 57.5, - 58.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCATAGTTACGCGC-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 57.5, - 57.5, - 56.5, - 56.5, - 57.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCATTACAGGCATGGT-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 56.5, - 56.5, - 55.5, - 55.5, - 56.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTAGAGCCTCGTG-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 55.5, - 55.5, - 54.5, - 54.5, - 55.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATTTGTGGCCCTA-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 52.5, - 52.5, - 51.5, - 51.5, - 52.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAACTTTCCCATTAT-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 50.5, - 50.5, - 49.5, - 49.5, - 50.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCACAAGTCCACTGGG-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 49.5, - 49.5, - 48.5, - 48.5, - 49.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGTTGGTCAGCGATCC-1
CTCTTTTCAGTTCA_r3
TAATT[219:2D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 20.5, - 20.5, - 19.5, - 19.5, - 20.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACCGTAATCCGAGCCA-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 19.5, - 19.5, - 18.5, - 18.5, - 19.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GAATAAGTCAACCAAC-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 18.5, - 18.5, - 17.5, - 17.5, - 18.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAGACCTCATCACCC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 206.5, - 206.5, - 205.5, - 205.5, - 206.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGGCGTCACGCCAGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 189.5, - 189.5, - 188.5, - 188.5, - 189.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACAGCTACAGCTGGCT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 186.5, - 186.5, - 185.5, - 185.5, - 186.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTATCTAGGTGACCA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 185.5, - 185.5, - 184.5, - 184.5, - 185.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGATGTCTACTTAC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 184.5, - 184.5, - 183.5, - 183.5, - 184.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGAATAGGTCGCTTCT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 183.5, - 183.5, - 182.5, - 182.5, - 183.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGAGTAGACCCACC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 182.5, - 182.5, - 181.5, - 181.5, - 182.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAACCTCAGGACGAAA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 181.5, - 181.5, - 180.5, - 180.5, - 181.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAAACTCACGATGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 180.5, - 180.5, - 179.5, - 179.5, - 180.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACACCTCACGACTCG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 179.5, - 179.5, - 178.5, - 178.5, - 179.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAGGCGTTCACCTC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 178.5, - 178.5, - 177.5, - 177.5, - 178.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACCACTAGCCAGAAC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 177.5, - 177.5, - 176.5, - 176.5, - 177.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATGACAAGGAGTTGC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 176.5, - 176.5, - 175.5, - 175.5, - 176.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCGTTCAAGGACGAAA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 175.5, - 175.5, - 174.5, - 174.5, - 175.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTATTAAGTGAATTG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 174.5, - 174.5, - 173.5, - 173.5, - 174.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGCGGTAAGCCACGCT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 173.5, - 173.5, - 172.5, - 172.5, - 173.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGCGTTTGTCGCGAAA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 172.5, - 172.5, - 171.5, - 171.5, - 172.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTAGGCCATTCCTGC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 171.5, - 171.5, - 170.5, - 170.5, - 171.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTGTAAAGGCTAGGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 170.5, - 170.5, - 169.5, - 169.5, - 170.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAAACAAGGTTCT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 169.5, - 169.5, - 168.5, - 168.5, - 169.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTCCATCGTAGATC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 168.5, - 168.5, - 167.5, - 167.5, - 168.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTATTGCAGCCTTTC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 167.5, - 167.5, - 166.5, - 166.5, - 167.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGCATATGCTG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 166.5, - 166.5, - 165.5, - 165.5, - 166.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACTTGTTCCAAATGC-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 165.5, - 165.5, - 164.5, - 164.5, - 165.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TATTACCGTCTCTTAT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 164.5, - 164.5, - 163.5, - 163.5, - 164.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCATTACAGCTTTGGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 163.5, - 163.5, - 162.5, - 162.5, - 163.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTTCGGTCTTACCTA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 162.5, - 162.5, - 161.5, - 161.5, - 162.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGCATCAAGCGAGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 161.5, - 161.5, - 160.5, - 160.5, - 161.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTCCGTAAGTGTA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 160.5, - 160.5, - 159.5, - 159.5, - 160.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTCTCAATCCTTGCCA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 159.5, - 159.5, - 158.5, - 158.5, - 159.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTTGCGCCAAACTGCT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 158.5, - 158.5, - 157.5, - 157.5, - 158.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAACTAGACTAGAT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 153.5, - 153.5, - 152.5, - 152.5, - 153.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGCCAGGTAAATGTG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 117.5, - 117.5, - 116.5, - 116.5, - 117.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGCTCTCAGGTAAA-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 114.5, - 114.5, - 113.5, - 113.5, - 114.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGCCAATCCGAATGT-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 75.5, - 75.5, - 74.5, - 74.5, - 75.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#181bab", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTCGGGTCACCGTTGG-1
CTCTTTTCAGTTCA_r3
AGGAG[210:19D]AATGC", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 69.5, - 69.5, - 68.5, - 68.5, - 69.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9ded21", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCAGCGTGACTACT-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]ACGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 67.5, - 67.5, - 66.5, - 66.5, - 67.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9ded21", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGTCTGCGGCA-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]ACGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 66.5, - 66.5, - 65.5, - 65.5, - 66.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCAATTCATCCCACT-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 63.5, - 63.5, - 62.5, - 62.5, - 63.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGCGATTCAATCTCT-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 62.5, - 62.5, - 61.5, - 61.5, - 62.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGCTTCAGCGTGAGT-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 61.5, - 61.5, - 60.5, - 60.5, - 61.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGTCCCATCTCGATGA-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 60.5, - 60.5, - 59.5, - 59.5, - 60.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACCTATTCCGTAGGC-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 59.5, - 59.5, - 58.5, - 58.5, - 59.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AACTTTCTCGACGGAA-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 53.5, - 53.5, - 52.5, - 52.5, - 53.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGGACGTGTGAAATCA-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 51.5, - 51.5, - 50.5, - 50.5, - 51.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAAAGTCTCGTTC-1
CTCTTTTCAGTTCA_r3
TAATT[219:2D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 48.5, - 48.5, - 47.5, - 47.5, - 48.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAACACTTAAGC-1
CTCTTTTCAGTTCA_r3
TAATT[219:2D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 47.5, - 47.5, - 46.5, - 46.5, - 47.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATCGTATCCTTGCCA-1
CTCTTTTCAGTTCA_r3
TAATT[219:2D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 46.5, - 46.5, - 45.5, - 45.5, - 46.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGACTTTGTATAGGGC-1
CTCTTTTCAGTTCA_r3
TAATT[219:2D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 45.5, - 45.5, - 44.5, - 44.5, - 45.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#8047b5", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCGGTAGCAGACACTT-1
CTCTTTTCAGTTCA_r3
AATTC[220:1D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 44.5, - 44.5, - 43.5, - 43.5, - 44.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATGTCGAGATG-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 43.5, - 43.5, - 42.5, - 42.5, - 43.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#728cf3", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAAGACTCCTAGTGA-1
CTCTTTTCAGTTCA_r3
CTATT[215:6D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 41.5, - 41.5, - 40.5, - 40.5, - 41.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#edfa1a", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGGGTTCAGGACGTA-1
CTCTTTTCAGTTCA_r3
TAATT[219:1D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 40.5, - 40.5, - 39.5, - 39.5, - 40.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATTCAGAGACAGACC-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 39.5, - 39.5, - 38.5, - 38.5, - 39.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAATCTGTGCGAAAC-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 17.5, - 17.5, - 16.5, - 16.5, - 17.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCACTCAAGGTAA-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 8.5, - 8.5, - 7.5, - 7.5, - 8.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGCCGTCACCAGGTC-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 7.5, - 7.5, - 6.5, - 6.5, - 7.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCCAAACAAACGTGG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 195.5, - 195.5, - 194.5, - 194.5, - 195.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCTGCTGTATATCCG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 194.5, - 194.5, - 193.5, - 193.5, - 194.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATCGTACATAACCTG-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 73.5, - 73.5, - 72.5, - 72.5, - 73.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGATCGGGTCTAACGT-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 70.5, - 70.5, - 69.5, - 69.5, - 70.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#50c119", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATCAAGGCTCC-1
CTCTTTTCAGTTCA_r3
ATTCG[221:1I]CCGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 64.5, - 64.5, - 63.5, - 63.5, - 64.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTACTTTGTTAAGAAC-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 42.5, - 42.5, - 41.5, - 41.5, - 42.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGTGATCATGGTCA-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 38.5, - 38.5, - 37.5, - 37.5, - 38.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#e39461", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAAGAGGTCGATTGT-1
CTCTTTTCAGTTCA_r3
CTATT[215:14D]AATGC", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 10.5, - 10.5, - 9.5, - 9.5, - 10.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#cb53b4", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATCAGGTCGGATCC-1
CTCTTTTCAGTTCA_r3
AATTC[220:3D]GAGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 9.5, - 9.5, - 8.5, - 8.5, - 9.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AAGGAGCTCTACCTGC-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 6.5, - 6.5, - 5.5, - 5.5, - 6.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAGAGTAGCCCTAAT-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 76.5, - 76.5, - 75.5, - 75.5, - 76.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACGGATGTCGCCATG-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 74.5, - 74.5, - 73.5, - 73.5, - 74.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATCGGGAGACACGAC-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 72.5, - 72.5, - 71.5, - 71.5, - 72.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGACAACCATGCCCGA-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 71.5, - 71.5, - 70.5, - 70.5, - 71.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GAGGTGACATCGATGT-1
CTCTTTTCAGTTCA_r3
TAATT[219:2D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 14.5, - 14.5, - 13.5, - 13.5, - 14.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#cb53b4", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGAGGATCCAGATCA-1
CTCTTTTCAGTTCA_r3
AATTC[220:3D]GAGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 13.5, - 13.5, - 12.5, - 12.5, - 13.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACTGTCCAGTATG-1
CTCTTTTCAGTTCA_r3
TAATT[219:2D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 12.5, - 12.5, - 11.5, - 11.5, - 12.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTCCGTAGCTCCG-1
CTCTTTTCAGTTCA_r3
TAATT[219:2D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 11.5, - 11.5, - 10.5, - 10.5, - 11.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#cb53b4", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCGTACGTCCGTCAG-1
CTCTTTTCAGTTCA_r3
AATTC[220:3D]GAGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 5.5, - 5.5, - 4.5, - 4.5, - 5.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCAGAAGTCGCATCG-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 79.5, - 79.5, - 78.5, - 78.5, - 79.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTTCTGAGTTAAGTG-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 15.5, - 15.5, - 14.5, - 14.5, - 15.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGGGTCCAGCTGCAC-1
CTCTTTTCAGTTCA_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 3.5, - 3.5, - 2.5, - 2.5, - 3.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGCCAAAGCCTTGAT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 81.5, - 81.5, - 80.5, - 80.5, - 81.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAAACCATAAAGGT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 80.5, - 80.5, - 79.5, - 79.5, - 80.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#4e1493", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATTTGTTGCCTCT-1
CTCTTTTCAGTTCA_r3
ACGAT[165:55D]GCGGA", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 78.5, - 78.5, - 77.5, - 77.5, - 78.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATATTCCAGGGTTAG-1
CTCTTTTCAGTTCA_r3
TAATT[219:2D]CGGAG", - "type": "scatter", - "x": [ - 7.200000000000001, - 6.4, - 6.4, - 7.200000000000001, - 7.200000000000001 - ], - "y": [ - 16.5, - 16.5, - 15.5, - 15.5, - 16.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTAGAAGAATGTGT-1
CTCTTTTCAGTTCA_r3
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 132.5, - 132.5, - 131.5, - 131.5, - 132.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATGGTAGGACCACA-1
TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 115.5, - 115.5, - 114.5, - 114.5, - 115.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTAGTTCAGTGACATA-1
TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 167.5, - 167.5, - 166.5, - 166.5, - 167.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGCATATGCTG-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 166.5, - 166.5, - 165.5, - 165.5, - 166.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACTTGTTCCAAATGC-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 165.5, - 165.5, - 164.5, - 164.5, - 165.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TATTACCGTCTCTTAT-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 164.5, - 164.5, - 163.5, - 163.5, - 164.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCATTACAGCTTTGGT-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 163.5, - 163.5, - 162.5, - 162.5, - 163.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTTCGGTCTTACCTA-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 162.5, - 162.5, - 161.5, - 161.5, - 162.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGCATCAAGCGAGT-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 161.5, - 161.5, - 160.5, - 160.5, - 161.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTCCGTAAGTGTA-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 160.5, - 160.5, - 159.5, - 159.5, - 160.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTCTCAATCCTTGCCA-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 159.5, - 159.5, - 158.5, - 158.5, - 159.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTTGCGCCAAACTGCT-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 158.5, - 158.5, - 157.5, - 157.5, - 158.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAACTAGACTAGAT-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 153.5, - 153.5, - 152.5, - 152.5, - 153.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGCCAGGTAAATGTG-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 117.5, - 117.5, - 116.5, - 116.5, - 117.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGCTCTCAGGTAAA-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 114.5, - 114.5, - 113.5, - 113.5, - 114.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGCCAATCCGAATGT-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 75.5, - 75.5, - 74.5, - 74.5, - 75.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTCGGGTCACCGTTGG-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 69.5, - 69.5, - 68.5, - 68.5, - 69.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCAGCGTGACTACT-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 67.5, - 67.5, - 66.5, - 66.5, - 67.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGTCTGCGGCA-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 66.5, - 66.5, - 65.5, - 65.5, - 66.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCAATTCATCCCACT-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 63.5, - 63.5, - 62.5, - 62.5, - 63.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGCGATTCAATCTCT-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 62.5, - 62.5, - 61.5, - 61.5, - 62.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGCTTCAGCGTGAGT-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 61.5, - 61.5, - 60.5, - 60.5, - 61.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGTCCCATCTCGATGA-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 60.5, - 60.5, - 59.5, - 59.5, - 60.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACCTATTCCGTAGGC-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 59.5, - 59.5, - 58.5, - 58.5, - 59.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AACTTTCTCGACGGAA-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 53.5, - 53.5, - 52.5, - 52.5, - 53.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGGACGTGTGAAATCA-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 51.5, - 51.5, - 50.5, - 50.5, - 51.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAAAGTCTCGTTC-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 48.5, - 48.5, - 47.5, - 47.5, - 48.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAACACTTAAGC-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 47.5, - 47.5, - 46.5, - 46.5, - 47.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATCGTATCCTTGCCA-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 46.5, - 46.5, - 45.5, - 45.5, - 46.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGACTTTGTATAGGGC-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 45.5, - 45.5, - 44.5, - 44.5, - 45.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCGGTAGCAGACACTT-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 44.5, - 44.5, - 43.5, - 43.5, - 44.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATGTCGAGATG-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 43.5, - 43.5, - 42.5, - 42.5, - 43.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAAGACTCCTAGTGA-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 41.5, - 41.5, - 40.5, - 40.5, - 41.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGGGTTCAGGACGTA-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 40.5, - 40.5, - 39.5, - 39.5, - 40.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATTCAGAGACAGACC-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 39.5, - 39.5, - 38.5, - 38.5, - 39.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAATCTGTGCGAAAC-1
TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 7.5, - 7.5, - 6.5, - 6.5, - 7.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCCAAACAAACGTGG-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 195.5, - 195.5, - 194.5, - 194.5, - 195.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCTGCTGTATATCCG-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 194.5, - 194.5, - 193.5, - 193.5, - 194.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATCGTACATAACCTG-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 73.5, - 73.5, - 72.5, - 72.5, - 73.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGATCGGGTCTAACGT-1
TTTTGTGTTTCATC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 8.400000000000002, - 7.600000000000001, - 7.600000000000001, - 8.400000000000002, - 8.400000000000002 - ], - "y": [ - 70.5, - 70.5, - 69.5, - 69.5, - 70.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATCAAGGCTCC-1
TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r1
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TTTTGTGTTTCATC_r2
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TTTTGTGTTTCATC_r2
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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TTTTGTGTTTCATC_r3
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 144.5, - 144.5, - 143.5, - 143.5, - 144.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGAGTCGATAA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 139.5, - 139.5, - 138.5, - 138.5, - 139.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATCTGAGGCTACGA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 138.5, - 138.5, - 137.5, - 137.5, - 138.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAGGAGTGCACTTA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 137.5, - 137.5, - 136.5, - 136.5, - 137.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCTGTGTATGAAAC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 136.5, - 136.5, - 135.5, - 135.5, - 136.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGACATTAGGAGTCTG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 135.5, - 135.5, - 134.5, - 134.5, - 135.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGGAATGAGCCACCTG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 134.5, - 134.5, - 133.5, - 133.5, - 134.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCCGATCACCGAATT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 133.5, - 133.5, - 132.5, - 132.5, - 133.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTATTGGTGAGTATA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 132.5, - 132.5, - 131.5, - 131.5, - 132.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATGGTAGGACCACA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 127.5, - 127.5, - 126.5, - 126.5, - 127.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAGCAACACCATGTA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 122.5, - 122.5, - 121.5, - 121.5, - 122.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACACCAATCCACTGGG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 118.5, - 118.5, - 117.5, - 117.5, - 118.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTGTGCTCAACTGGCC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 115.5, - 115.5, - 114.5, - 114.5, - 115.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTAGTTCAGTGACATA-1
GTGCAAGCCTTATC_r2
nan", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 93.5, - 93.5, - 92.5, - 92.5, - 93.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCACTCTTCGAGAGCA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 92.5, - 92.5, - 91.5, - 91.5, - 92.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATACGGTTCCCGAG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 91.5, - 91.5, - 90.5, - 90.5, - 91.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTTTCTTCACAACGT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 90.5, - 90.5, - 89.5, - 89.5, - 90.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTTCTAGTACTTCTT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 89.5, - 89.5, - 88.5, - 88.5, - 89.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGTGGTAGTGTTGAGG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 87.5, - 87.5, - 86.5, - 86.5, - 87.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTCTAAGTTTGGGCC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 86.5, - 86.5, - 85.5, - 85.5, - 86.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGAATCGTTATCGGT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 83.5, - 83.5, - 82.5, - 82.5, - 83.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTGCTTCATCGGAAG-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 68.5, - 68.5, - 67.5, - 67.5, - 68.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACCTTAGTCTCCATC-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 54.5, - 54.5, - 53.5, - 53.5, - 54.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTTCGGCACAGGTTT-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 37.5, - 37.5, - 36.5, - 36.5, - 37.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTCTCAAGTGTAATGA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 207.5, - 207.5, - 206.5, - 206.5, - 207.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAGGATCTGAAAGA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 205.5, - 205.5, - 204.5, - 204.5, - 205.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TAAACCGTCGTACCGG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 204.5, - 204.5, - 203.5, - 203.5, - 204.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCATCTTCAACT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 193.5, - 193.5, - 192.5, - 192.5, - 193.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTACCGGTGGTCCGT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 192.5, - 192.5, - 191.5, - 191.5, - 192.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGTCCTCACGATGT-1
GTGCAAGCCTTATC_r2
nan", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 188.5, - 188.5, - 187.5, - 187.5, - 188.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCTCCTGTTTCCACC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 187.5, - 187.5, - 186.5, - 186.5, - 187.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGCGATAGTAGGTGC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 157.5, - 157.5, - 156.5, - 156.5, - 157.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGCCGCATGCAATC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 156.5, - 156.5, - 155.5, - 155.5, - 156.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCAAACTTCACGCATA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 155.5, - 155.5, - 154.5, - 154.5, - 155.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAAGTTCAACCA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 154.5, - 154.5, - 153.5, - 153.5, - 154.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTGGGGTTCACGGC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 152.5, - 152.5, - 151.5, - 151.5, - 152.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGGTATGAATG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 151.5, - 151.5, - 150.5, - 150.5, - 151.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTCACTAGTCCAGGA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 149.5, - 149.5, - 148.5, - 148.5, - 149.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCATTCGAGCGATGAC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 126.5, - 126.5, - 125.5, - 125.5, - 126.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACTCATTCGGTTCGG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 125.5, - 125.5, - 124.5, - 124.5, - 125.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGAATCCAAGGCTCC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 123.5, - 123.5, - 122.5, - 122.5, - 123.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCAGGCAATTA-1
GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 116.5, - 116.5, - 115.5, - 115.5, - 116.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AACCGCGTCCACTGGG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 88.5, - 88.5, - 87.5, - 87.5, - 88.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCCTCTAGTGCTGTAT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 85.5, - 85.5, - 84.5, - 84.5, - 85.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTCTGCATTCCTCG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 84.5, - 84.5, - 83.5, - 83.5, - 84.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCATGGGTCACAAGG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 82.5, - 82.5, - 81.5, - 81.5, - 82.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCACACGTAGCGTAG-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 65.5, - 65.5, - 64.5, - 64.5, - 65.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAGACCTCATATCGG-1
GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 56.5, - 56.5, - 55.5, - 55.5, - 56.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTAGAGCCTCGTG-1
GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 50.5, - 50.5, - 49.5, - 49.5, - 50.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCACAAGTCCACTGGG-1
GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
nan", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 20.5, - 20.5, - 19.5, - 19.5, - 20.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACCGTAATCCGAGCCA-1
GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 206.5, - 206.5, - 205.5, - 205.5, - 206.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGGCGTCACGCCAGT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 189.5, - 189.5, - 188.5, - 188.5, - 189.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACAGCTACAGCTGGCT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 186.5, - 186.5, - 185.5, - 185.5, - 186.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTATCTAGGTGACCA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 185.5, - 185.5, - 184.5, - 184.5, - 185.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGATGTCTACTTAC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 184.5, - 184.5, - 183.5, - 183.5, - 184.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGAATAGGTCGCTTCT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 183.5, - 183.5, - 182.5, - 182.5, - 183.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGAGTAGACCCACC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 182.5, - 182.5, - 181.5, - 181.5, - 182.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAACCTCAGGACGAAA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 181.5, - 181.5, - 180.5, - 180.5, - 181.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAAACTCACGATGT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 180.5, - 180.5, - 179.5, - 179.5, - 180.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACACCTCACGACTCG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 179.5, - 179.5, - 178.5, - 178.5, - 179.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAGGCGTTCACCTC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 178.5, - 178.5, - 177.5, - 177.5, - 178.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACCACTAGCCAGAAC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 177.5, - 177.5, - 176.5, - 176.5, - 177.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATGACAAGGAGTTGC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 176.5, - 176.5, - 175.5, - 175.5, - 176.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCGTTCAAGGACGAAA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 175.5, - 175.5, - 174.5, - 174.5, - 175.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTATTAAGTGAATTG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 174.5, - 174.5, - 173.5, - 173.5, - 174.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGCGGTAAGCCACGCT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 173.5, - 173.5, - 172.5, - 172.5, - 173.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGCGTTTGTCGCGAAA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 172.5, - 172.5, - 171.5, - 171.5, - 172.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTAGGCCATTCCTGC-1
GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 168.5, - 168.5, - 167.5, - 167.5, - 168.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTATTGCAGCCTTTC-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 167.5, - 167.5, - 166.5, - 166.5, - 167.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGCATATGCTG-1
GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 164.5, - 164.5, - 163.5, - 163.5, - 164.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCATTACAGCTTTGGT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 163.5, - 163.5, - 162.5, - 162.5, - 163.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTTCGGTCTTACCTA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 162.5, - 162.5, - 161.5, - 161.5, - 162.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGCATCAAGCGAGT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 161.5, - 161.5, - 160.5, - 160.5, - 161.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTCCGTAAGTGTA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 160.5, - 160.5, - 159.5, - 159.5, - 160.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTCTCAATCCTTGCCA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 159.5, - 159.5, - 158.5, - 158.5, - 159.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTTGCGCCAAACTGCT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 158.5, - 158.5, - 157.5, - 157.5, - 158.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAACTAGACTAGAT-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 153.5, - 153.5, - 152.5, - 152.5, - 153.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGCCAGGTAAATGTG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 117.5, - 117.5, - 116.5, - 116.5, - 117.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGCTCTCAGGTAAA-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 114.5, - 114.5, - 113.5, - 113.5, - 114.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGCCAATCCGAATGT-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 75.5, - 75.5, - 74.5, - 74.5, - 75.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTCGGGTCACCGTTGG-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 69.5, - 69.5, - 68.5, - 68.5, - 69.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCAGCGTGACTACT-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 67.5, - 67.5, - 66.5, - 66.5, - 67.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGTCTGCGGCA-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 66.5, - 66.5, - 65.5, - 65.5, - 66.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCAATTCATCCCACT-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 63.5, - 63.5, - 62.5, - 62.5, - 63.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGCGATTCAATCTCT-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 62.5, - 62.5, - 61.5, - 61.5, - 62.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGCTTCAGCGTGAGT-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 61.5, - 61.5, - 60.5, - 60.5, - 61.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGTCCCATCTCGATGA-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 60.5, - 60.5, - 59.5, - 59.5, - 60.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACCTATTCCGTAGGC-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 59.5, - 59.5, - 58.5, - 58.5, - 59.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AACTTTCTCGACGGAA-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 53.5, - 53.5, - 52.5, - 52.5, - 53.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGGACGTGTGAAATCA-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 51.5, - 51.5, - 50.5, - 50.5, - 51.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAAAGTCTCGTTC-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 48.5, - 48.5, - 47.5, - 47.5, - 48.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAACACTTAAGC-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 47.5, - 47.5, - 46.5, - 46.5, - 47.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATCGTATCCTTGCCA-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 46.5, - 46.5, - 45.5, - 45.5, - 46.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGACTTTGTATAGGGC-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 45.5, - 45.5, - 44.5, - 44.5, - 45.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCGGTAGCAGACACTT-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 44.5, - 44.5, - 43.5, - 43.5, - 44.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATGTCGAGATG-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 43.5, - 43.5, - 42.5, - 42.5, - 43.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAAGACTCCTAGTGA-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 41.5, - 41.5, - 40.5, - 40.5, - 41.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGGGTTCAGGACGTA-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 40.5, - 40.5, - 39.5, - 39.5, - 40.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATTCAGAGACAGACC-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 39.5, - 39.5, - 38.5, - 38.5, - 39.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAATCTGTGCGAAAC-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 17.5, - 17.5, - 16.5, - 16.5, - 17.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#67d445", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCACTCAAGGTAA-1
GTGCAAGCCTTATC_r2
GTCAT[159:5D]GAACT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 8.5, - 8.5, - 7.5, - 7.5, - 8.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#67d445", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGCCGTCACCAGGTC-1
GTGCAAGCCTTATC_r2
GTCAT[159:5D]GAACT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 7.5, - 7.5, - 6.5, - 6.5, - 7.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCCAAACAAACGTGG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 195.5, - 195.5, - 194.5, - 194.5, - 195.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#d3e84e", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCTGCTGTATATCCG-1
GTGCAAGCCTTATC_r2
GTACG[108:59D]CTCTG", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 194.5, - 194.5, - 193.5, - 193.5, - 194.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATCGTACATAACCTG-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 73.5, - 73.5, - 72.5, - 72.5, - 73.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGATCGGGTCTAACGT-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 70.5, - 70.5, - 69.5, - 69.5, - 70.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATCAAGGCTCC-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 64.5, - 64.5, - 63.5, - 63.5, - 64.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTACTTTGTTAAGAAC-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 42.5, - 42.5, - 41.5, - 41.5, - 42.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGTGATCATGGTCA-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 38.5, - 38.5, - 37.5, - 37.5, - 38.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#67d445", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAAGAGGTCGATTGT-1
GTGCAAGCCTTATC_r2
GTCAT[159:5D]GAACT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 10.5, - 10.5, - 9.5, - 9.5, - 10.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#67d445", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATCAGGTCGGATCC-1
GTGCAAGCCTTATC_r2
GTCAT[159:5D]GAACT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 9.5, - 9.5, - 8.5, - 8.5, - 9.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#67d445", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AAGGAGCTCTACCTGC-1
GTGCAAGCCTTATC_r2
GTCAT[159:5D]GAACT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 6.5, - 6.5, - 5.5, - 5.5, - 6.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAGAGTAGCCCTAAT-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 76.5, - 76.5, - 75.5, - 75.5, - 76.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACGGATGTCGCCATG-1
GTGCAAGCCTTATC_r2
GATAT[167:10D]GCCGT", - "type": "scatter", - "x": [ - 13.200000000000001, - 12.4, - 12.4, - 13.200000000000001, - 13.200000000000001 - ], - "y": [ - 74.5, - 74.5, - 73.5, - 73.5, - 74.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#66b450", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATCGGGAGACACGAC-1
GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r2
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 144.5, - 144.5, - 143.5, - 143.5, - 144.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCTCGAGTCGATAA-1
GTGCAAGCCTTATC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 139.5, - 139.5, - 138.5, - 138.5, - 139.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATCTGAGGCTACGA-1
GTGCAAGCCTTATC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 138.5, - 138.5, - 137.5, - 137.5, - 138.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAGGAGTGCACTTA-1
GTGCAAGCCTTATC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 137.5, - 137.5, - 136.5, - 136.5, - 137.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTCTGTGTATGAAAC-1
GTGCAAGCCTTATC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 136.5, - 136.5, - 135.5, - 135.5, - 136.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGACATTAGGAGTCTG-1
GTGCAAGCCTTATC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 135.5, - 135.5, - 134.5, - 134.5, - 135.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGGAATGAGCCACCTG-1
GTGCAAGCCTTATC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 134.5, - 134.5, - 133.5, - 133.5, - 134.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCCGATCACCGAATT-1
GTGCAAGCCTTATC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 133.5, - 133.5, - 132.5, - 132.5, - 133.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTATTGGTGAGTATA-1
GTGCAAGCCTTATC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 132.5, - 132.5, - 131.5, - 131.5, - 132.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATGGTAGGACCACA-1
GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 122.5, - 122.5, - 121.5, - 121.5, - 122.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACACCAATCCACTGGG-1
GTGCAAGCCTTATC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 118.5, - 118.5, - 117.5, - 117.5, - 118.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTGTGCTCAACTGGCC-1
GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
nan", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 93.5, - 93.5, - 92.5, - 92.5, - 93.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCACTCTTCGAGAGCA-1
GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 192.5, - 192.5, - 191.5, - 191.5, - 192.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGTCCTCACGATGT-1
GTGCAAGCCTTATC_r3
nan", - "type": "scatter", - "x": [ - 14.4, - 13.6, - 13.6, - 14.4, - 14.4 - ], - "y": [ - 188.5, - 188.5, - 187.5, - 187.5, - 188.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#732798", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCTCCTGTTTCCACC-1
GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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GTGCAAGCCTTATC_r3
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 192.5, - 192.5, - 191.5, - 191.5, - 192.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGTCCTCACGATGT-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 188.5, - 188.5, - 187.5, - 187.5, - 188.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCTCCTGTTTCCACC-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 187.5, - 187.5, - 186.5, - 186.5, - 187.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGCGATAGTAGGTGC-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 157.5, - 157.5, - 156.5, - 156.5, - 157.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGCCGCATGCAATC-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 156.5, - 156.5, - 155.5, - 155.5, - 156.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCAAACTTCACGCATA-1
ACATTATTAGATAC_r1
nan", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 155.5, - 155.5, - 154.5, - 154.5, - 155.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAAGTTCAACCA-1
ACATTATTAGATAC_r1
nan", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 154.5, - 154.5, - 153.5, - 153.5, - 154.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTGGGGTTCACGGC-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 152.5, - 152.5, - 151.5, - 151.5, - 152.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGGTATGAATG-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 151.5, - 151.5, - 150.5, - 150.5, - 151.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTCACTAGTCCAGGA-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 149.5, - 149.5, - 148.5, - 148.5, - 149.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCATTCGAGCGATGAC-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 126.5, - 126.5, - 125.5, - 125.5, - 126.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACTCATTCGGTTCGG-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 125.5, - 125.5, - 124.5, - 124.5, - 125.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGAATCCAAGGCTCC-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 123.5, - 123.5, - 122.5, - 122.5, - 123.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCAGGCAATTA-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 120.5, - 120.5, - 119.5, - 119.5, - 120.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAGGAGGAGTTGC-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 119.5, - 119.5, - 118.5, - 118.5, - 119.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCACACAAGCCC-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 116.5, - 116.5, - 115.5, - 115.5, - 116.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0875b4", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AACCGCGTCCACTGGG-1
ACATTATTAGATAC_r1
ACGCC[110:3D]TTTTG", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 88.5, - 88.5, - 87.5, - 87.5, - 88.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCCTCTAGTGCTGTAT-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 85.5, - 85.5, - 84.5, - 84.5, - 85.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTCTGCATTCCTCG-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 84.5, - 84.5, - 83.5, - 83.5, - 84.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCATGGGTCACAAGG-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 82.5, - 82.5, - 81.5, - 81.5, - 82.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCACACGTAGCGTAG-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 65.5, - 65.5, - 64.5, - 64.5, - 65.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAGACCTCATATCGG-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 58.5, - 58.5, - 57.5, - 57.5, - 58.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCATAGTTACGCGC-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 57.5, - 57.5, - 56.5, - 56.5, - 57.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCATTACAGGCATGGT-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 56.5, - 56.5, - 55.5, - 55.5, - 56.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTAGAGCCTCGTG-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 55.5, - 55.5, - 54.5, - 54.5, - 55.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATTTGTGGCCCTA-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 52.5, - 52.5, - 51.5, - 51.5, - 52.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAACTTTCCCATTAT-1
ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 176.5, - 176.5, - 175.5, - 175.5, - 176.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCGTTCAAGGACGAAA-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 175.5, - 175.5, - 174.5, - 174.5, - 175.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTATTAAGTGAATTG-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 174.5, - 174.5, - 173.5, - 173.5, - 174.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGCGGTAAGCCACGCT-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 173.5, - 173.5, - 172.5, - 172.5, - 173.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGCGTTTGTCGCGAAA-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 172.5, - 172.5, - 171.5, - 171.5, - 172.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTAGGCCATTCCTGC-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 171.5, - 171.5, - 170.5, - 170.5, - 171.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTGTAAAGGCTAGGT-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 170.5, - 170.5, - 169.5, - 169.5, - 170.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAAACAAGGTTCT-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 169.5, - 169.5, - 168.5, - 168.5, - 169.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTCCATCGTAGATC-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 168.5, - 168.5, - 167.5, - 167.5, - 168.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTATTGCAGCCTTTC-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 167.5, - 167.5, - 166.5, - 166.5, - 167.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGCATATGCTG-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 166.5, - 166.5, - 165.5, - 165.5, - 166.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACTTGTTCCAAATGC-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 165.5, - 165.5, - 164.5, - 164.5, - 165.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TATTACCGTCTCTTAT-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 164.5, - 164.5, - 163.5, - 163.5, - 164.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCATTACAGCTTTGGT-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 163.5, - 163.5, - 162.5, - 162.5, - 163.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTTCGGTCTTACCTA-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 162.5, - 162.5, - 161.5, - 161.5, - 162.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGCATCAAGCGAGT-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 161.5, - 161.5, - 160.5, - 160.5, - 161.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTCCGTAAGTGTA-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 160.5, - 160.5, - 159.5, - 159.5, - 160.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTCTCAATCCTTGCCA-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 159.5, - 159.5, - 158.5, - 158.5, - 159.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTTGCGCCAAACTGCT-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 158.5, - 158.5, - 157.5, - 157.5, - 158.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAACTAGACTAGAT-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 153.5, - 153.5, - 152.5, - 152.5, - 153.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGCCAGGTAAATGTG-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 117.5, - 117.5, - 116.5, - 116.5, - 117.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGCTCTCAGGTAAA-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 114.5, - 114.5, - 113.5, - 113.5, - 114.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGCCAATCCGAATGT-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 75.5, - 75.5, - 74.5, - 74.5, - 75.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTCGGGTCACCGTTGG-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 69.5, - 69.5, - 68.5, - 68.5, - 69.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGCAGCGTGACTACT-1
ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 66.5, - 66.5, - 65.5, - 65.5, - 66.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGCAATTCATCCCACT-1
ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 60.5, - 60.5, - 59.5, - 59.5, - 60.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACCTATTCCGTAGGC-1
ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
CGGTC[99:36D]CGGCA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 8.5, - 8.5, - 7.5, - 7.5, - 8.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#39c7d0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGCCGTCACCAGGTC-1
ACATTATTAGATAC_r1
CGGTC[99:36D]CGGCA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 7.5, - 7.5, - 6.5, - 6.5, - 7.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCCAAACAAACGTGG-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 195.5, - 195.5, - 194.5, - 194.5, - 195.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCTGCTGTATATCCG-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 194.5, - 194.5, - 193.5, - 193.5, - 194.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATCGTACATAACCTG-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 73.5, - 73.5, - 72.5, - 72.5, - 73.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGATCGGGTCTAACGT-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 70.5, - 70.5, - 69.5, - 69.5, - 70.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATCAAGGCTCC-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 64.5, - 64.5, - 63.5, - 63.5, - 64.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTACTTTGTTAAGAAC-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 42.5, - 42.5, - 41.5, - 41.5, - 42.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGTGATCATGGTCA-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 38.5, - 38.5, - 37.5, - 37.5, - 38.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#39c7d0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAAGAGGTCGATTGT-1
ACATTATTAGATAC_r1
CGGTC[99:36D]CGGCA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 10.5, - 10.5, - 9.5, - 9.5, - 10.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#39c7d0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATCAGGTCGGATCC-1
ACATTATTAGATAC_r1
CGGTC[99:36D]CGGCA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 9.5, - 9.5, - 8.5, - 8.5, - 9.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#39c7d0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AAGGAGCTCTACCTGC-1
ACATTATTAGATAC_r1
CGGTC[99:36D]CGGCA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 6.5, - 6.5, - 5.5, - 5.5, - 6.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAGAGTAGCCCTAAT-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 76.5, - 76.5, - 75.5, - 75.5, - 76.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACGGATGTCGCCATG-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 74.5, - 74.5, - 73.5, - 73.5, - 74.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATCGGGAGACACGAC-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 72.5, - 72.5, - 71.5, - 71.5, - 72.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGACAACCATGCCCGA-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 71.5, - 71.5, - 70.5, - 70.5, - 71.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#92bc63", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GAGGTGACATCGATGT-1
ACATTATTAGATAC_r1
CGCCG[111:4D]ATGGA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 14.5, - 14.5, - 13.5, - 13.5, - 14.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#09d235", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGAGGATCCAGATCA-1
ACATTATTAGATAC_r1
CGCCG[111:2I]AAAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 13.5, - 13.5, - 12.5, - 12.5, - 13.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#74a85d", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACTGTCCAGTATG-1
ACATTATTAGATAC_r1
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ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 11.5, - 11.5, - 10.5, - 10.5, - 11.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCGTACGTCCGTCAG-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 5.5, - 5.5, - 4.5, - 4.5, - 5.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCAGAAGTCGCATCG-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 79.5, - 79.5, - 78.5, - 78.5, - 79.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTTCTGAGTTAAGTG-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 15.5, - 15.5, - 14.5, - 14.5, - 15.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#f62480", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGGGTCCAGCTGCAC-1
ACATTATTAGATAC_r1
CGCCG[111:1I]AAAAAA", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 3.5, - 3.5, - 2.5, - 2.5, - 3.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGCCAAAGCCTTGAT-1
ACATTATTAGATAC_r1
nan", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 81.5, - 81.5, - 80.5, - 80.5, - 81.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAAACCATAAAGGT-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 80.5, - 80.5, - 79.5, - 79.5, - 80.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#78ee70", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATTTGTTGCCTCT-1
ACATTATTAGATAC_r1
GTACG[108:12D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 78.5, - 78.5, - 77.5, - 77.5, - 78.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#59d605", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATATTCCAGGGTTAG-1
ACATTATTAGATAC_r1
CGCCG[111:1D]AAAAT", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 16.5, - 16.5, - 15.5, - 15.5, - 16.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#bc894f", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTAGAAGAATGTGT-1
ACATTATTAGATAC_r1
CCGAA[113:108D]CGGAG", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 4.5, - 4.5, - 3.5, - 3.5, - 4.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#59d605", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATTCGCTCCCACTTG-1
ACATTATTAGATAC_r1
CGCCG[111:1D]AAAAT", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 1.5, - 1.5, - 0.5, - 0.5, - 1.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9a9763", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAATGGTCACCAGGC-1
ACATTATTAGATAC_r1
CGCCG[111:2D]AAATG", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 0.5, - 0.5, - -0.5, - -0.5, - 0.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#1d9cfc", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGGAACTCGGCTTGG-1
ACATTATTAGATAC_r1
CCGAA[113:7D]CTGAC", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 2.5, - 2.5, - 1.5, - 1.5, - 2.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b3541d", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGCCAGAGGCCATAG-1
ACATTATTAGATAC_r1
TACGC[109:3D]AAAAT", - "type": "scatter", - "x": [ - 15.6, - 14.799999999999999, - 14.799999999999999, - 15.6, - 15.6 - ], - "y": [ - 77.5, - 77.5, - 76.5, - 76.5, - 77.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#6ca2e7", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGGAGACAAGGGTCA-1
ACATTATTAGATAC_r2
CATAA[161:18D]CGTGA", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 34.5, - 34.5, - 33.5, - 33.5, - 34.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#6ca2e7", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTCATTTCCAGGGCT-1
ACATTATTAGATAC_r2
CATAA[161:18D]CGTGA", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 30.5, - 30.5, - 29.5, - 29.5, - 30.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#6ca2e7", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCCTAATCTCAAACG-1
ACATTATTAGATAC_r2
CATAA[161:18D]CGTGA", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 29.5, - 29.5, - 28.5, - 28.5, - 29.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#6ca2e7", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATGACACATCATCCC-1
ACATTATTAGATAC_r2
CATAA[161:18D]CGTGA", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 28.5, - 28.5, - 27.5, - 27.5, - 28.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AATCCAGCACCAGTTA-1
ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 197.5, - 197.5, - 196.5, - 196.5, - 197.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTACAGGTGTTTGTG-1
ACATTATTAGATAC_r2
nan", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 107.5, - 107.5, - 106.5, - 106.5, - 107.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGAGCAACGGT-1
ACATTATTAGATAC_r2
nan", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 105.5, - 105.5, - 104.5, - 104.5, - 105.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGCCAGAGGTCATCT-1
ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 91.5, - 91.5, - 90.5, - 90.5, - 91.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCTTTCTTCACAACGT-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 90.5, - 90.5, - 89.5, - 89.5, - 90.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTTTCTAGTACTTCTT-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 89.5, - 89.5, - 88.5, - 88.5, - 89.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGTGGTAGTGTTGAGG-1
ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
CATAA[161:18D]CGTGA", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 54.5, - 54.5, - 53.5, - 53.5, - 54.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#6ca2e7", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCTTCGGCACAGGTTT-1
ACATTATTAGATAC_r2
CATAA[161:18D]CGTGA", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 37.5, - 37.5, - 36.5, - 36.5, - 37.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTCTCAAGTGTAATGA-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 207.5, - 207.5, - 206.5, - 206.5, - 207.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTAGGATCTGAAAGA-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 205.5, - 205.5, - 204.5, - 204.5, - 205.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TAAACCGTCGTACCGG-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 204.5, - 204.5, - 203.5, - 203.5, - 204.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCATCTTCAACT-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 193.5, - 193.5, - 192.5, - 192.5, - 193.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTTACCGGTGGTCCGT-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 192.5, - 192.5, - 191.5, - 191.5, - 192.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACTGTCCTCACGATGT-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 188.5, - 188.5, - 187.5, - 187.5, - 188.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGCTCCTGTTTCCACC-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 187.5, - 187.5, - 186.5, - 186.5, - 187.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGCGATAGTAGGTGC-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 157.5, - 157.5, - 156.5, - 156.5, - 157.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGAGCCGCATGCAATC-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 156.5, - 156.5, - 155.5, - 155.5, - 156.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCAAACTTCACGCATA-1
ACATTATTAGATAC_r2
nan", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 155.5, - 155.5, - 154.5, - 154.5, - 155.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCTTGAAGTTCAACCA-1
ACATTATTAGATAC_r2
nan", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 154.5, - 154.5, - 153.5, - 153.5, - 154.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTGGGGTTCACGGC-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 152.5, - 152.5, - 151.5, - 151.5, - 152.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GGTGAAGGTATGAATG-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 151.5, - 151.5, - 150.5, - 150.5, - 151.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTCACTAGTCCAGGA-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 149.5, - 149.5, - 148.5, - 148.5, - 149.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCATTCGAGCGATGAC-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 126.5, - 126.5, - 125.5, - 125.5, - 126.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACTCATTCGGTTCGG-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 125.5, - 125.5, - 124.5, - 124.5, - 125.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CAGAATCCAAGGCTCC-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 123.5, - 123.5, - 122.5, - 122.5, - 123.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGATTGCAGGCAATTA-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 120.5, - 120.5, - 119.5, - 119.5, - 120.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATGAGGAGGAGTTGC-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 119.5, - 119.5, - 118.5, - 118.5, - 119.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCCACACAAGCCC-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 116.5, - 116.5, - 115.5, - 115.5, - 116.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AACCGCGTCCACTGGG-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 88.5, - 88.5, - 87.5, - 87.5, - 88.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCCTCTAGTGCTGTAT-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 85.5, - 85.5, - 84.5, - 84.5, - 85.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTCTGCATTCCTCG-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 84.5, - 84.5, - 83.5, - 83.5, - 84.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#0f57e0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATCATGGGTCACAAGG-1
ACATTATTAGATAC_r2
CGATA[166:2D]TCTGG", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 82.5, - 82.5, - 81.5, - 81.5, - 82.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#6ca2e7", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCCACACGTAGCGTAG-1
ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
CATAA[161:18D]CGTGA", - "type": "scatter", - "x": [ - 16.799999999999997, - 15.999999999999998, - 15.999999999999998, - 16.799999999999997, - 16.799999999999997 - ], - "y": [ - 56.5, - 56.5, - 55.5, - 55.5, - 56.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#6ca2e7", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTAGAGCCTCGTG-1
ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
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ACATTATTAGATAC_r2
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r1
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r2
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
TAATT[219:2D]CGGAG", - "type": "scatter", - "x": [ - 21.599999999999994, - 20.799999999999997, - 20.799999999999997, - 21.599999999999994, - 21.599999999999994 - ], - "y": [ - 200.5, - 200.5, - 199.5, - 199.5, - 200.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATGGGAGCAAAGGAAG-1
ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
ATTCG[None]CGGAG", - "type": "scatter", - "x": [ - 21.599999999999994, - 20.799999999999997, - 20.799999999999997, - 21.599999999999994, - 21.599999999999994 - ], - "y": [ - 140.5, - 140.5, - 139.5, - 139.5, - 140.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#5a8c80", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTGAAACCATTTGCCC-1
ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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ACATCTGCGAGATT_r3
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
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TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 46.5, - 46.5, - 45.5, - 45.5, - 46.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGACTTTGTATAGGGC-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 45.5, - 45.5, - 44.5, - 44.5, - 45.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CCGGTAGCAGACACTT-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 44.5, - 44.5, - 43.5, - 43.5, - 44.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATGTCGAGATG-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 43.5, - 43.5, - 42.5, - 42.5, - 43.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAAGACTCCTAGTGA-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 41.5, - 41.5, - 40.5, - 40.5, - 41.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGGGTTCAGGACGTA-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 40.5, - 40.5, - 39.5, - 39.5, - 40.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATTCAGAGACAGACC-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 39.5, - 39.5, - 38.5, - 38.5, - 39.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TCAATCTGTGCGAAAC-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 17.5, - 17.5, - 16.5, - 16.5, - 17.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#04b4e9", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGAGCACTCAAGGTAA-1
TATGCCGCCAGTTT_r2
AACGA[164:4D]TCTGG", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 8.5, - 8.5, - 7.5, - 7.5, - 8.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGCCGTCACCAGGTC-1
TATGCCGCCAGTTT_r2
nan", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 7.5, - 7.5, - 6.5, - 6.5, - 7.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9c8d68", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCCAAACAAACGTGG-1
TATGCCGCCAGTTT_r2
TTTGT[105:61D]GCTCT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 195.5, - 195.5, - 194.5, - 194.5, - 195.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9c8d68", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGCTGCTGTATATCCG-1
TATGCCGCCAGTTT_r2
TTTGT[105:61D]GCTCT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 194.5, - 194.5, - 193.5, - 193.5, - 194.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GATCGTACATAACCTG-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 73.5, - 73.5, - 72.5, - 72.5, - 73.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGATCGGGTCTAACGT-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 70.5, - 70.5, - 69.5, - 69.5, - 70.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCATGATCAAGGCTCC-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 64.5, - 64.5, - 63.5, - 63.5, - 64.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTACTTTGTTAAGAAC-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 42.5, - 42.5, - 41.5, - 41.5, - 42.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AGGGTGATCATGGTCA-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 38.5, - 38.5, - 37.5, - 37.5, - 38.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATAAGAGGTCGATTGT-1
TATGCCGCCAGTTT_r2
nan", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 10.5, - 10.5, - 9.5, - 9.5, - 10.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACATCAGGTCGGATCC-1
TATGCCGCCAGTTT_r2
nan", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 9.5, - 9.5, - 8.5, - 8.5, - 9.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.AAGGAGCTCTACCTGC-1
TATGCCGCCAGTTT_r2
nan", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 6.5, - 6.5, - 5.5, - 5.5, - 6.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAGAGTAGCCCTAAT-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 76.5, - 76.5, - 75.5, - 75.5, - 76.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TACGGATGTCGCCATG-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 74.5, - 74.5, - 73.5, - 73.5, - 74.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATCGGGAGACACGAC-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 72.5, - 72.5, - 71.5, - 71.5, - 72.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGACAACCATGCCCGA-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 71.5, - 71.5, - 70.5, - 70.5, - 71.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GAGGTGACATCGATGT-1
TATGCCGCCAGTTT_r2
nan", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 14.5, - 14.5, - 13.5, - 13.5, - 14.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#9c8d68", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGAGGATCCAGATCA-1
TATGCCGCCAGTTT_r2
TTTGT[105:61D]GCTCT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 13.5, - 13.5, - 12.5, - 12.5, - 13.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTAACTGTCCAGTATG-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 12.5, - 12.5, - 11.5, - 11.5, - 12.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGTTCCGTAGCTCCG-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 11.5, - 11.5, - 10.5, - 10.5, - 11.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCGTACGTCCGTCAG-1
TATGCCGCCAGTTT_r2
nan", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 5.5, - 5.5, - 4.5, - 4.5, - 5.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTCAGAAGTCGCATCG-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 79.5, - 79.5, - 78.5, - 78.5, - 79.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#04b4e9", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ATTTCTGAGTTAAGTG-1
TATGCCGCCAGTTT_r2
AACGA[164:4D]TCTGG", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 15.5, - 15.5, - 14.5, - 14.5, - 15.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GTGGGTCCAGCTGCAC-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 3.5, - 3.5, - 2.5, - 2.5, - 3.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#04b4e9", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.GCGCCAAAGCCTTGAT-1
TATGCCGCCAGTTT_r2
AACGA[164:4D]TCTGG", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 81.5, - 81.5, - 80.5, - 80.5, - 81.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACAAACCATAAAGGT-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 80.5, - 80.5, - 79.5, - 79.5, - 80.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ffffff", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CACATTTGTTGCCTCT-1
TATGCCGCCAGTTT_r2
nan", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 78.5, - 78.5, - 77.5, - 77.5, - 78.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATATTCCAGGGTTAG-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 16.5, - 16.5, - 15.5, - 15.5, - 16.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#ba43ac", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CGTTAGAAGAATGTGT-1
TATGCCGCCAGTTT_r2
ATATC[168:1D]CTGGT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 4.5, - 4.5, - 3.5, - 3.5, - 4.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#cb7a61", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CATTCGCTCCCACTTG-1
TATGCCGCCAGTTT_r2
TCTCT[171:50D]CGGAG", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 1.5, - 1.5, - 0.5, - 0.5, - 1.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#b81e55", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.CTAATGGTCACCAGGC-1
TATGCCGCCAGTTT_r2
TAACG[163:2D]ATCTC", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 0.5, - 0.5, - -0.5, - -0.5, - 0.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#fd77c2", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TTGGAACTCGGCTTGG-1
TATGCCGCCAGTTT_r2
ACGAT[165:1D]TCTCT", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 2.5, - 2.5, - 1.5, - 1.5, - 2.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#bc894f", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.TGGCCAGAGGCCATAG-1
TATGCCGCCAGTTT_r2
CCGAA[113:108D]CGGAG", - "type": "scatter", - "x": [ - 31.19999999999999, - 30.39999999999999, - 30.39999999999999, - 31.19999999999999, - 31.19999999999999 - ], - "y": [ - 77.5, - 77.5, - 76.5, - 76.5, - 77.5 - ] - }, - { - "fill": "toself", - "fillcolor": "#c0c0c0", - "hoverinfo": "text", - "hoveron": "fills", - "line": { - "width": 0 - }, - "mode": "lines", - "showlegend": false, - "text": "L37.ACGGAGACAAGGGTCA-1
TATGCCGCCAGTTT_r3
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TATGCCGCCAGTTT_r3
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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -202420,7 +291,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -202434,7 +305,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.13" } }, "nbformat": 4, From f6d10be0ce2d877eb1063a4ea94282db85079cc4 Mon Sep 17 00:00:00 2001 From: Matthew Gregory Jones Date: Sun, 4 Sep 2022 18:21:42 -0700 Subject: [PATCH 17/37] added functionality for simulating cooperativity --- .../simulator/ecDNABirthDeathSimulator.py | 35 +++++++++++++------ 1 file changed, 24 insertions(+), 11 deletions(-) diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 2256bbe0..35070666 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -100,7 +100,13 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): the tree resulting from pruning dead lineages random_seed: A seed for reproducibility initial_copy_number: Initial copy number for parental lineage. - splitting_function: As implemented, the function that describes segregation of each species at cell division. TO DO: fix this implementation to allow for non-independent segregation. + coopoerativity_coefficient: A coefficient describing how likely it is for one species to be co-inherited + with one specific species (currently modeled as the first in the array). + TODO: how do we make this generalizable to multiple species each with different pairwise covariances? + splitting_function: As implemented, the function that describes segregation of each species at cell division. + TO DO: fix this implementation to allow for non-independent segregation. + fitness_array: Fitnesses with respect to copy number of each species in a cell. This should be a matrix in + R^e (where e is the number of ecDNA species being modelled). Raises: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) TreeSimulatorError if invalid stopping conditions are provided or if a @@ -121,7 +127,8 @@ def __init__( collapse_unifurcations: bool = True, random_seed: int = None, initial_copy_number: np.array = np.array([1]), - splitting_function: Callable[[int], int] = lambda x: np.random.binomial(x, p=0.5), + cooperativity_coefficient: float = 0.0, + splitting_function: Callable[[int], int] = lambda c, x: c + np.random.binomial(x, p=0.5), fitness_array: np.array = np.array([0,1]), ): if num_extant is None and experiment_time is None: @@ -148,6 +155,7 @@ def __init__( self.collapse_unifurcations = collapse_unifurcations self.random_seed = random_seed self.initial_copy_number = initial_copy_number + self.cooperativity_coefficient = cooperativity_coefficient self.splitting_function = splitting_function self.fitness_array = fitness_array @@ -167,7 +175,8 @@ def initialize_tree(self, names) -> nx.DiGraph: def update_fitness(self, ecdna_array: np.array) -> float: """Updates a lineage birth scale, which represents its (Malthusian) fitness. - Fitness is computed as a function of copy number, using the fitness_array (which defines fitness for CN=0 or CN >0 for each species, with epistasis). + Fitness is computed as a function of copy number, using the fitness_array + (which defines fitness for CN=0 or CN >0 for each species, with epistasis). Args: ecdna_array: The birth_scale to be updated @@ -241,11 +250,9 @@ def sample_lineage_event( # (but that requires shifting times elsewhere in order to permit correct model comparison to non-ecDNA simulators. if lineage["total_time"] == 0: # Update birth rate - updated_birth_scale = self.update_fitness(tree.nodes[lineage["id"]][ - "ecdna_array" - ]) - - # child_ecdna_array = self.get_ecdna_array(lineage["id"],tree) + updated_birth_scale = self.update_fitness( + tree.nodes[lineage["id"]]["ecdna_array"] + ) # Annotate parameters for a given node in the tree tree.add_node(unique_id) @@ -380,9 +387,15 @@ def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: new_ecdna_array = parental_ecdna_array - child_ecdna_array else: - new_ecdna_array = np.array( - [self.splitting_function(n) for n in parental_ecdna_array] - ) + new_ecdna_array = np.array([0] * len(parental_ecdna_array)) + + new_ecdna_array[0]= self.splitting_function(0, parental_ecdna_array[0]) + + for species in range(1, len(parental_ecdna_array)): + new_ecdna_array[species] = self.splitting_function( + self.cooperativity_coefficient*(new_ecdna_array[0] / parental_ecdna_array[0]), + (1 - self.cooperativity_coefficient)*(parental_ecdna_array[species]) + ) # check that new ecdnay array entries do not exceed parental entries if np.any(new_ecdna_array > parental_ecdna_array): From b7ae314f9ab9a60cbfedf4f31fd631aca5a021bb Mon Sep 17 00:00:00 2001 From: Matthew Gregory Jones Date: Wed, 7 Sep 2022 09:22:04 -0700 Subject: [PATCH 18/37] bug fix in cooperativity sim --- cassiopeia/simulator/ecDNABirthDeathSimulator.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 35070666..09cab597 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -3,6 +3,7 @@ process, including differing fitness on lineages on the tree. Allows for a variety of division and fitness regimes to be specified by the user. """ +from random import random from typing import Callable, Dict, Generator, List, Optional, Union import networkx as nx @@ -392,11 +393,19 @@ def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: new_ecdna_array[0]= self.splitting_function(0, parental_ecdna_array[0]) for species in range(1, len(parental_ecdna_array)): - new_ecdna_array[species] = self.splitting_function( - self.cooperativity_coefficient*(new_ecdna_array[0] / parental_ecdna_array[0]), - (1 - self.cooperativity_coefficient)*(parental_ecdna_array[species]) + + cosegregating_compartment = int(self.cooperativity_coefficient*(new_ecdna_array[0] / max(1, parental_ecdna_array[0])) * parental_ecdna_array[species]) + sister_cell_cosegregating = int(self.cooperativity_coefficient*( (parental_ecdna_array[0]-new_ecdna_array[0]) / max(1, parental_ecdna_array[0])) * parental_ecdna_array[species]) + + random_compartment = parental_ecdna_array[species] - cosegregating_compartment - sister_cell_cosegregating + + inherited_fraction = self.splitting_function( + cosegregating_compartment, + random_compartment, ) + new_ecdna_array[species] = inherited_fraction + # check that new ecdnay array entries do not exceed parental entries if np.any(new_ecdna_array > parental_ecdna_array): raise ecDNABirthDeathSimulatorError( From e9b5e5edf8fef8e25990bd58784895f1f026187b Mon Sep 17 00:00:00 2001 From: Matthew Gregory Jones Date: Wed, 7 Sep 2022 10:54:31 -0700 Subject: [PATCH 19/37] added tests for cosegregation --- .../simulator/ecDNABirthDeathSimulator.py | 10 +- .../ecdna_birth_death_simulator_test.py | 121 +++++++++++++++++- 2 files changed, 125 insertions(+), 6 deletions(-) diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 09cab597..dde72981 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -101,7 +101,7 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): the tree resulting from pruning dead lineages random_seed: A seed for reproducibility initial_copy_number: Initial copy number for parental lineage. - coopoerativity_coefficient: A coefficient describing how likely it is for one species to be co-inherited + cosegregation_coefficient: A coefficient describing how likely it is for one species to be co-inherited with one specific species (currently modeled as the first in the array). TODO: how do we make this generalizable to multiple species each with different pairwise covariances? splitting_function: As implemented, the function that describes segregation of each species at cell division. @@ -128,7 +128,7 @@ def __init__( collapse_unifurcations: bool = True, random_seed: int = None, initial_copy_number: np.array = np.array([1]), - cooperativity_coefficient: float = 0.0, + cosegregation_coefficient: float = 0.0, splitting_function: Callable[[int], int] = lambda c, x: c + np.random.binomial(x, p=0.5), fitness_array: np.array = np.array([0,1]), ): @@ -156,7 +156,7 @@ def __init__( self.collapse_unifurcations = collapse_unifurcations self.random_seed = random_seed self.initial_copy_number = initial_copy_number - self.cooperativity_coefficient = cooperativity_coefficient + self.cosegregation_coefficient = cosegregation_coefficient self.splitting_function = splitting_function self.fitness_array = fitness_array @@ -394,8 +394,8 @@ def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: for species in range(1, len(parental_ecdna_array)): - cosegregating_compartment = int(self.cooperativity_coefficient*(new_ecdna_array[0] / max(1, parental_ecdna_array[0])) * parental_ecdna_array[species]) - sister_cell_cosegregating = int(self.cooperativity_coefficient*( (parental_ecdna_array[0]-new_ecdna_array[0]) / max(1, parental_ecdna_array[0])) * parental_ecdna_array[species]) + cosegregating_compartment = int(self.cosegregation_coefficient*(new_ecdna_array[0] / max(1, parental_ecdna_array[0])) * parental_ecdna_array[species]) + sister_cell_cosegregating = int(self.cosegregation_coefficient*( (parental_ecdna_array[0]-new_ecdna_array[0]) / max(1, parental_ecdna_array[0])) * parental_ecdna_array[species]) random_compartment = parental_ecdna_array[species] - cosegregating_compartment - sister_cell_cosegregating diff --git a/test/simulator_tests/ecdna_birth_death_simulator_test.py b/test/simulator_tests/ecdna_birth_death_simulator_test.py index 903a7482..2c547b09 100644 --- a/test/simulator_tests/ecdna_birth_death_simulator_test.py +++ b/test/simulator_tests/ecdna_birth_death_simulator_test.py @@ -232,7 +232,126 @@ def test_populate_tree_from_simulation(self): }, orient='index', columns = ['ecDNA_0', 'ecDNA_1', 'ecDNA_2']) pd.testing.assert_frame_equal(expected_meta_data, cassiopeia_tree.cell_meta.astype(int)) - + + def test_basic_cosegregation(self): + + np.random.seed(41) + + # test basic cosegregation + sim = ecDNABirthDeathSimulator( + birth_waiting_distribution=lambda _: 1, + initial_birth_scale=1, + num_extant=16, + experiment_time=5, + cosegregation_coefficient=0.5, + fitness_array = np.array([[0, 0.5], [0.5, 0]]), + ) + + names = node_name_generator() + + tree = nx.DiGraph() + root = next(names) + tree.add_node(root) + tree.nodes[root]["birth_scale"] = 1 + tree.nodes[root]["time"] = 0 + tree.nodes[root]["ecdna_array"] = np.array([5, 5]) + current_lineages = PriorityQueue() + observed_nodes = [] + starting_lineage = { + "id": root, + "birth_scale": tree.nodes[root]["birth_scale"], + "total_time": tree.nodes[root]["time"], + "active": True, + } + + # simulate time till first cell division + sim.sample_lineage_event( + starting_lineage, current_lineages, tree, names, observed_nodes + ) + + _, _, new_lineage = current_lineages.get() + + # now, let's do one normal division + sim.sample_lineage_event( + new_lineage, current_lineages, tree, names, observed_nodes + ) + sim.sample_lineage_event( + new_lineage, current_lineages, tree, names, observed_nodes + ) + + # get out the children + _, _, child_lineage_1 = current_lineages.get() + _, _, child_lineage_2 = current_lineages.get() + + # expected arrays derivation (with random seed 41): + # ecdna1 segregates as (4, 6): + # ecdna2 co-segregation compartment - (2, 3) + # remainder binomial: np.random.binomial(5, p=0.5) = 1 [with random seed 41, second draw] + + expected_child_1_array = [4, 3] + expected_child_2_array = [6, 7] + + print(tree.nodes[child_lineage_1["id"]]["ecdna_array"]) + + self.assertTrue( + np.all(tree.nodes[child_lineage_1["id"]]["ecdna_array"] == expected_child_1_array) + ) + self.assertTrue( + np.all(tree.nodes[child_lineage_2["id"]]["ecdna_array"] == expected_child_2_array) + ) + + def test_perfect_cosegregation(self): + + np.random.seed(41) + + # test perfect cosegregation + sim = ecDNABirthDeathSimulator( + birth_waiting_distribution=lambda _: 1, + initial_birth_scale=1, + num_extant=16, + experiment_time=5, + cosegregation_coefficient=1.0, + fitness_array = np.array([[0, 0.5], [0.5, 0]]), + ) + + names = node_name_generator() + + tree = nx.DiGraph() + root = next(names) + tree.add_node(root) + tree.nodes[root]["birth_scale"] = 1 + tree.nodes[root]["time"] = 0 + tree.nodes[root]["ecdna_array"] = np.array([5, 5]) + current_lineages = PriorityQueue() + observed_nodes = [] + starting_lineage = { + "id": root, + "birth_scale": tree.nodes[root]["birth_scale"], + "total_time": tree.nodes[root]["time"], + "active": True, + } + + # simulate time till first cell division + sim.sample_lineage_event( + starting_lineage, current_lineages, tree, names, observed_nodes + ) + + _, _, new_lineage = current_lineages.get() + + # now, let's do one normal division + sim.sample_lineage_event( + new_lineage, current_lineages, tree, names, observed_nodes + ) + + sim.sample_lineage_event( + new_lineage, current_lineages, tree, names, observed_nodes + ) + + _, _, child_lineage_1 = current_lineages.get() + _, _, child_lineage_2 = current_lineages.get() + + self.assertEqual(tree.nodes[child_lineage_1["id"]]["ecdna_array"][0], tree.nodes[child_lineage_1["id"]]["ecdna_array"][1]) + self.assertEqual(tree.nodes[child_lineage_2["id"]]["ecdna_array"][0], tree.nodes[child_lineage_2["id"]]["ecdna_array"][1]) if __name__ == "__main__": From 1c7e53285b7a2e060d7ff2248d8a29017f347a37 Mon Sep 17 00:00:00 2001 From: Matthew Gregory Jones Date: Fri, 30 Sep 2022 15:53:00 -0700 Subject: [PATCH 20/37] added binomial noise model --- .../simulator/ecDNABirthDeathSimulator.py | 9 ++- .../ecdna_birth_death_simulator_test.py | 63 ++++++++++++++++--- 2 files changed, 63 insertions(+), 9 deletions(-) diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index dde72981..8282a4e5 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -108,6 +108,7 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): TO DO: fix this implementation to allow for non-independent segregation. fitness_array: Fitnesses with respect to copy number of each species in a cell. This should be a matrix in R^e (where e is the number of ecDNA species being modelled). + capture_efficiency: Probability of observing an ecDNA species. Used as the the probability of a binomial process. Raises: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) TreeSimulatorError if invalid stopping conditions are provided or if a @@ -131,6 +132,7 @@ def __init__( cosegregation_coefficient: float = 0.0, splitting_function: Callable[[int], int] = lambda c, x: c + np.random.binomial(x, p=0.5), fitness_array: np.array = np.array([0,1]), + capture_efficiency: float = 1.0, ): if num_extant is None and experiment_time is None: raise TreeSimulatorError("Please specify at least one stopping condition") @@ -158,7 +160,8 @@ def __init__( self.initial_copy_number = initial_copy_number self.cosegregation_coefficient = cosegregation_coefficient self.splitting_function = splitting_function - self.fitness_array = fitness_array + self.fitness_array = fitness_array + self.capture_efficiency = capture_efficiency # update to store cn_array in node. (tree.nodes[root]["cn_array"]) def initialize_tree(self, names) -> nx.DiGraph: @@ -454,6 +457,10 @@ def populate_tree_from_simulation(self, tree: nx.DiGraph, observed_nodes: List[s for leaf in cassiopeia_tree.leaves: cell_metadata.loc[leaf] = cassiopeia_tree.get_attribute(leaf, 'ecdna_array') + # apply noise model + for i in range(len(self.initial_copy_number)): + cell_metadata[f'Observed_ecDNA_{i}'] = cell_metadata.apply(lambda x: np.random.binomial(x[f'ecDNA_{i}'], self.capture_efficiency), axis=1) + cassiopeia_tree.cell_meta = cell_metadata.astype(int).copy() return cassiopeia_tree \ No newline at end of file diff --git a/test/simulator_tests/ecdna_birth_death_simulator_test.py b/test/simulator_tests/ecdna_birth_death_simulator_test.py index 2c547b09..1704dce4 100644 --- a/test/simulator_tests/ecdna_birth_death_simulator_test.py +++ b/test/simulator_tests/ecdna_birth_death_simulator_test.py @@ -9,14 +9,10 @@ import numpy as np import pandas as pd - - from cassiopeia.data.CassiopeiaTree import CassiopeiaTree -from cassiopeia.mixins import ecDNABirthDeathSimulatorError from cassiopeia.simulator.ecDNABirthDeathSimulator import ( ecDNABirthDeathSimulator, ) - import cassiopeia.data.utilities as utilities @@ -227,10 +223,10 @@ def test_populate_tree_from_simulation(self): cassiopeia_tree = sim.populate_tree_from_simulation(tree, [child_1, child_2]) expected_meta_data = pd.DataFrame.from_dict({ - child_1: [2, 0, 6], - child_2: [4, 4, 4] - }, orient='index', columns = ['ecDNA_0', 'ecDNA_1', 'ecDNA_2']) - + child_1: [2, 0, 6, 2, 0, 6], + child_2: [4, 4, 4, 4, 4, 4] + }, orient='index', columns = ['ecDNA_0', 'ecDNA_1', 'ecDNA_2', 'Observed_ecDNA_0', 'Observed_ecDNA_1', 'Observed_ecDNA_2']) + pd.testing.assert_frame_equal(expected_meta_data, cassiopeia_tree.cell_meta.astype(int)) def test_basic_cosegregation(self): @@ -353,6 +349,57 @@ def test_perfect_cosegregation(self): self.assertEqual(tree.nodes[child_lineage_1["id"]]["ecdna_array"][0], tree.nodes[child_lineage_1["id"]]["ecdna_array"][1]) self.assertEqual(tree.nodes[child_lineage_2["id"]]["ecdna_array"][0], tree.nodes[child_lineage_2["id"]]["ecdna_array"][1]) + def test_low_capture_efficiency(self): + + np.random.seed(41) + sim = ecDNABirthDeathSimulator( + birth_waiting_distribution=lambda _: 1, + initial_birth_scale=1, + num_extant=16, + experiment_time=5, + initial_copy_number=[3,2,5], + capture_efficiency=0.5, + ) + + names = node_name_generator() + + tree = nx.DiGraph() + root = next(names) + tree.add_node(root) + + child_1, child_2 = next(names), next(names) + tree.add_edges_from([(root, child_1), (root, child_2)]) + + tree.nodes[root]["birth_scale"] = 1 + tree.nodes[root]["time"] = 0 + tree.nodes[root]["ecdna_array"] = np.array([3, 2, 5]) + + tree.nodes[child_1]['birth_scale'] = 1 + tree.nodes[child_1]["time"] = 2 + tree.nodes[child_1]["ecdna_array"] = np.array([2,0,6]) + + tree.nodes[child_2]['birth_scale'] = 1 + tree.nodes[child_2]["time"] = 2 + tree.nodes[child_2]["ecdna_array"] = np.array([4, 4, 4]) + + cassiopeia_tree = sim.populate_tree_from_simulation(tree, [child_1, child_2]) + + # reset seed to create expected observations + np.random.seed(41) + c1_0 = np.random.binomial(2, 0.5) + c2_0 = np.random.binomial(4, 0.5) + c1_1 = np.random.binomial(0, 0.5) + c2_1 = np.random.binomial(4, 0.5) + c1_2 = np.random.binomial(6, 0.5) + c2_2 = np.random.binomial(4, 0.5) + + expected_meta_data = pd.DataFrame.from_dict({ + child_1: [2, 0, 6, c1_0, c1_1, c1_2], + child_2: [4, 4, 4, c2_0, c2_1, c2_2] + }, orient='index', columns = ['ecDNA_0', 'ecDNA_1', 'ecDNA_2', 'Observed_ecDNA_0', 'Observed_ecDNA_1', 'Observed_ecDNA_2']) + + pd.testing.assert_frame_equal(expected_meta_data, cassiopeia_tree.cell_meta.astype(int)) + if __name__ == "__main__": unittest.main() From a2c1193b21feca7dc063cfd73f98a40bf8cb8ce3 Mon Sep 17 00:00:00 2001 From: Matthew Gregory Jones Date: Thu, 1 Dec 2022 14:16:17 -0800 Subject: [PATCH 21/37] sped up simulator --- .../simulator/BirthDeathFitnessSimulator.py | 196 +++++++++----- .../simulator/ecDNABirthDeathSimulator.py | 241 +++++++++++++----- .../ecdna_birth_death_simulator_test.py | 51 ++-- 3 files changed, 352 insertions(+), 136 deletions(-) diff --git a/cassiopeia/simulator/BirthDeathFitnessSimulator.py b/cassiopeia/simulator/BirthDeathFitnessSimulator.py index 730dbe35..df6f00d2 100644 --- a/cassiopeia/simulator/BirthDeathFitnessSimulator.py +++ b/cassiopeia/simulator/BirthDeathFitnessSimulator.py @@ -3,11 +3,13 @@ process, including differing fitness on lineages on the tree. Allows for a variety of division and fitness regimes to be specified by the user. """ -from typing import Callable, Dict, Generator, List, Optional, Union +from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union import networkx as nx import numpy as np -from queue import PriorityQueue + +# from queue import PriorityQueue +import heapq from cassiopeia.data.CassiopeiaTree import CassiopeiaTree from cassiopeia.mixins import TreeSimulatorError @@ -146,38 +148,34 @@ def __init__( self.experiment_time = experiment_time self.collapse_unifurcations = collapse_unifurcations self.random_seed = random_seed - - def initialize_tree( - self, - names - ) -> nx.DiGraph: - ''' initializes a tree (nx.DiGraph() object with one node). Auxiliary data for each node is grabbed from self (initial conditions / params) or hardcoded - - Args: names is a generator (function object that stores internal state) that will be used to generate names for the tree nodes - Output: tree (DiGraph object with one node, the root) - root (name of root node in tree) - ''' + + def initialize_tree(self, names) -> nx.DiGraph: + """initializes a tree (nx.DiGraph() object with one node). Auxiliary data for each node is grabbed from self (initial conditions / params) or hardcoded + + Args: names is a generator (function object that stores internal state) that will be used to generate names for the tree nodes + Output: tree (DiGraph object with one node, the root) + root (name of root node in tree) + """ tree = nx.DiGraph() root = next(names) tree.add_node(root) tree.nodes[root]["birth_scale"] = self.initial_birth_scale tree.nodes[root]["time"] = 0 - + return tree, root - def make_initial_lineage_dict( - self, - id_value - ): - """ + def make_initial_lineage_dict(self, id_value): + """ uses self initial-conditions and hardcoded default parameters to create an initial lineage dict - Args: id_value: name of new lineage + Args: id_value: name of new lineage Output: a lineage dict """ - lineage = self.make_lineage_dict(id_value, self.initial_birth_scale, 0, True) + lineage = self.make_lineage_dict( + id_value, self.initial_birth_scale, 0, True + ) return lineage - + def simulate_tree( self, ) -> CassiopeiaTree: @@ -214,13 +212,14 @@ def node_name_generator() -> Generator[str, None, None]: np.random.seed(self.random_seed) tree, root = self.initialize_tree(names) - - current_lineages = PriorityQueue() + + # current_lineages = PriorityQueue() + current_lineages = [] # instantiate heap # Records the nodes that are observed at the end of the experiment - # TO DO: update to accept arbitrary fields in the dict. + # TO DO: update to accept arbitrary fields in the dict. observed_nodes = [] - + """ starting_lineage = { "id": root, @@ -231,30 +230,65 @@ def node_name_generator() -> Generator[str, None, None]: """ starting_lineage = self.make_initial_lineage_dict(root) - + # Sample the waiting time until the first division self.sample_lineage_event( starting_lineage, current_lineages, tree, names, observed_nodes ) # Perform the process until there are no active extant lineages left - while not current_lineages.empty(): + # while not current_lineages.empty(): + # # If number of extant lineages is the stopping criterion, at the + # # first instance of having n extant tips, stop the experiment + # # and set the total lineage time for each lineage to be equal to + # # the minimum, to produce ultrametric trees. Also, the birth_scale + # # parameter of each leaf is rolled back to equal its parent's. + # if self.num_extant: + # if current_lineages.qsize() == self.num_extant: + # remaining_lineages = [] + # while not current_lineages.empty(): + # _, _, lineage = current_lineages.get() + # remaining_lineages.append(lineage) + # min_total_time = remaining_lineages[0]["total_time"] + # for lineage in remaining_lineages: + # parent = list(tree.predecessors(lineage["id"]))[0] + + # # I don't think this is make_daughter_node; is this some kind of a node update? + # tree.nodes[lineage["id"]]["time"] += ( + # min_total_time - lineage["total_time"] + # ) + # tree.nodes[lineage["id"]]["birth_scale"] = tree.nodes[ + # parent + # ]["birth_scale"] + # observed_nodes.append(lineage["id"]) + # break + # # Pop the minimum age lineage to simulate forward time + # _, _, lineage = current_lineages.get() + # # If the lineage is no longer active, just remove it from the queue. + # # This represents the time at which the lineage dies. + # if lineage["active"]: + # for i in range(2): + # self.sample_lineage_event( + # lineage, current_lineages, tree, names, observed_nodes + # ) + + while len(current_lineages) > 0: # If number of extant lineages is the stopping criterion, at the # first instance of having n extant tips, stop the experiment # and set the total lineage time for each lineage to be equal to # the minimum, to produce ultrametric trees. Also, the birth_scale # parameter of each leaf is rolled back to equal its parent's. if self.num_extant: - if current_lineages.qsize() == self.num_extant: + if len(current_lineages) == self.num_extant: remaining_lineages = [] - while not current_lineages.empty(): - _, _, lineage = current_lineages.get() + while len(current_lineages) > 0: + _, _, lineage = heapq.heappop(current_lineages) remaining_lineages.append(lineage) min_total_time = remaining_lineages[0]["total_time"] for lineage in remaining_lineages: parent = list(tree.predecessors(lineage["id"]))[0] - - # I don't think this is make_daughter_node; is this some kind of a node update? + + # I don't think this is make_daughter_node; is this some kind of a node update? tree.nodes[lineage["id"]]["time"] += ( min_total_time - lineage["total_time"] ) @@ -264,7 +298,7 @@ def node_name_generator() -> Generator[str, None, None]: observed_nodes.append(lineage["id"]) break # Pop the minimum age lineage to simulate forward time - _, _, lineage = current_lineages.get() + _, _, lineage = heapq.heappop(current_lineages) # If the lineage is no longer active, just remove it from the queue. # This represents the time at which the lineage dies. if lineage["active"]: @@ -273,14 +307,17 @@ def node_name_generator() -> Generator[str, None, None]: lineage, current_lineages, tree, names, observed_nodes ) - cassiopeia_tree = self.populate_tree_from_simulation(tree, observed_nodes) + cassiopeia_tree = self.populate_tree_from_simulation( + tree, observed_nodes + ) return cassiopeia_tree def sample_lineage_event( self, lineage: Dict[str, Union[int, float]], - current_lineages: PriorityQueue, + current_lineages: List[Tuple[int, Any]], + # current_lineages: PriorityQueue, tree: nx.DiGraph, names: Generator, observed_nodes: List[str], @@ -350,7 +387,8 @@ def sample_lineage_event( tree.add_edge(lineage["id"], unique_id) tree.nodes[unique_id]["time"] = self.experiment_time - current_lineages.put( + heapq.heappush( + current_lineages, ( self.experiment_time, unique_id, @@ -360,8 +398,20 @@ def sample_lineage_event( "total_time": self.experiment_time, "active": False, }, - ) + ), ) + # current_lineages.put( + # ( + # self.experiment_time, + # unique_id, + # { + # "id": unique_id, + # "birth_scale": lineage["birth_scale"], + # "total_time": self.experiment_time, + # "active": False, + # }, + # ) + # ) # Indicate this node is observed at the end of experiment observed_nodes.append(unique_id) @@ -381,7 +431,8 @@ def sample_lineage_event( birth_waiting_time + lineage["total_time"] ) # Add the newly generated cell to the list of living lineages - current_lineages.put( + heapq.heappush( + current_lineages, ( birth_waiting_time + lineage["total_time"], unique_id, @@ -392,9 +443,23 @@ def sample_lineage_event( + lineage["total_time"], "active": True, }, - ) + ), ) + # current_lineages.put( + # ( + # birth_waiting_time + lineage["total_time"], + # unique_id, + # { + # "id": unique_id, + # "birth_scale": updated_birth_scale, + # "total_time": birth_waiting_time + # + lineage["total_time"], + # "active": True, + # }, + # ) + # ) + else: tree.add_node(unique_id) tree.nodes[unique_id]["birth_scale"] = lineage["birth_scale"] @@ -402,7 +467,8 @@ def sample_lineage_event( tree.nodes[unique_id]["time"] = ( death_waiting_time + lineage["total_time"] ) - current_lineages.put( + heapq.heappush( + current_lineages, ( death_waiting_time + lineage["total_time"], unique_id, @@ -413,9 +479,23 @@ def sample_lineage_event( + lineage["total_time"], "active": False, }, - ) + ), ) + # current_lineages.put( + # ( + # death_waiting_time + lineage["total_time"], + # unique_id, + # { + # "id": unique_id, + # "birth_scale": lineage["birth_scale"], + # "total_time": death_waiting_time + # + lineage["total_time"], + # "active": False, + # }, + # ) + # ) + def update_fitness(self, birth_scale: float) -> float: """Updates a lineage birth scale, which represents its fitness. @@ -452,23 +532,23 @@ def update_fitness(self, birth_scale: float) -> float: self.fitness_base ** self.fitness_distribution() ) return birth_scale * base_selection_coefficient - + def make_lineage_dict( self, - id_value, + id_value, birth_scale, total_time, active_flag, ): - """makes a dict (lineage) from the given parameters. keys are hardcoded. - - Args: id_value: id of new lineage - birth_scale: birth_scale parameter of new lineage - total_time: age of lineage - active_flag: bool to indicate whether lineage is active - - Returns: a dict (lineage) with the parameter values under the hard-coded keys - + """makes a dict (lineage) from the given parameters. keys are hardcoded. + + Args: id_value: id of new lineage + birth_scale: birth_scale parameter of new lineage + total_time: age of lineage + active_flag: bool to indicate whether lineage is active + + Returns: a dict (lineage) with the parameter values under the hard-coded keys + """ lineage = { "id": id_value, @@ -478,13 +558,15 @@ def make_lineage_dict( } return lineage - def populate_tree_from_simulation(self, tree: nx.DiGraph, observed_nodes: List[str]) -> CassiopeiaTree: + def populate_tree_from_simulation( + self, tree: nx.DiGraph, observed_nodes: List[str] + ) -> CassiopeiaTree: """Populates tree with appropriate meta data. - + Args: tree: The tree simulated with ecDNA and fitness values populated as attributes. observed_nodes: The observed leaves of the tree. - + Returns: A CassiopeiaTree with relevant node attributes filled in. """ @@ -508,4 +590,4 @@ def populate_tree_from_simulation(self, tree: nx.DiGraph, observed_nodes: List[s "All lineages died before stopping condition" ) - return cassiopeia_tree \ No newline at end of file + return cassiopeia_tree diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 8282a4e5..64c19ceb 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -4,12 +4,14 @@ variety of division and fitness regimes to be specified by the user. """ from random import random -from typing import Callable, Dict, Generator, List, Optional, Union +from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd -from queue import PriorityQueue + +# from queue import PriorityQueue +import heapq from cassiopeia.data.CassiopeiaTree import CassiopeiaTree from cassiopeia.mixins import ecDNABirthDeathSimulatorError, TreeSimulatorError @@ -120,7 +122,9 @@ def __init__( self, birth_waiting_distribution: Callable[[float], float], initial_birth_scale: float, - death_waiting_distribution: Optional[Callable[[], float]] = lambda: np.inf, + death_waiting_distribution: Optional[ + Callable[[], float] + ] = lambda: np.inf, mutation_distribution: Optional[Callable[[], int]] = None, fitness_distribution: Optional[Callable[[], float]] = None, fitness_base: float = np.e, @@ -130,22 +134,31 @@ def __init__( random_seed: int = None, initial_copy_number: np.array = np.array([1]), cosegregation_coefficient: float = 0.0, - splitting_function: Callable[[int], int] = lambda c, x: c + np.random.binomial(x, p=0.5), - fitness_array: np.array = np.array([0,1]), + splitting_function: Callable[[int], int] = lambda c, x: c + + np.random.binomial(x, p=0.5), + fitness_array: np.array = np.array([0, 1]), capture_efficiency: float = 1.0, ): if num_extant is None and experiment_time is None: - raise TreeSimulatorError("Please specify at least one stopping condition") + raise TreeSimulatorError( + "Please specify at least one stopping condition" + ) if mutation_distribution is not None and fitness_distribution is None: - raise TreeSimulatorError("Please specify a fitness strength distribution") + raise TreeSimulatorError( + "Please specify a fitness strength distribution" + ) if num_extant is not None and num_extant <= 0: raise TreeSimulatorError( "Please specify number of extant lineages greater than 0" ) if num_extant is not None and type(num_extant) is not int: - raise TreeSimulatorError("Please specify an integer number of extant tips") + raise TreeSimulatorError( + "Please specify an integer number of extant tips" + ) if experiment_time is not None and experiment_time <= 0: - raise TreeSimulatorError("Please specify an experiment time greater than 0") + raise TreeSimulatorError( + "Please specify an experiment time greater than 0" + ) self.birth_waiting_distribution = birth_waiting_distribution self.initial_birth_scale = initial_birth_scale @@ -180,7 +193,7 @@ def update_fitness(self, ecdna_array: np.array) -> float: """Updates a lineage birth scale, which represents its (Malthusian) fitness. Fitness is computed as a function of copy number, using the fitness_array - (which defines fitness for CN=0 or CN >0 for each species, with epistasis). + (which defines fitness for CN=0 or CN >0 for each species, with epistasis). Args: ecdna_array: The birth_scale to be updated @@ -192,12 +205,15 @@ def update_fitness(self, ecdna_array: np.array) -> float: TreeSimulatorError if a negative number of mutations is sampled """ - return self.initial_birth_scale * (1.0 + self.fitness_array[tuple((ecdna_array > 0).astype(int))]) + return self.initial_birth_scale * ( + 1.0 + self.fitness_array[tuple((ecdna_array > 0).astype(int))] + ) def sample_lineage_event( self, lineage: Dict[str, Union[int, float]], - current_lineages: PriorityQueue, + # current_lineages: PriorityQueue, + current_lineages: List[Tuple[int, Any]], tree: nx.DiGraph, names: Generator, observed_nodes: List[str], @@ -240,18 +256,25 @@ def sample_lineage_event( non-active lineage is passed in """ if not lineage["active"]: - raise TreeSimulatorError("Cannot sample event for non-active lineage") + raise TreeSimulatorError( + "Cannot sample event for non-active lineage" + ) unique_id = next(names) - birth_waiting_time = self.birth_waiting_distribution(lineage["birth_scale"]) + birth_waiting_time = self.birth_waiting_distribution( + lineage["birth_scale"] + ) death_waiting_time = self.death_waiting_distribution() if birth_waiting_time <= 0 or death_waiting_time <= 0: raise TreeSimulatorError("0 or negative waiting time detected") - # TO DO: this is a really hacky fix b/c it bypasses the length checks of whether the first birth_waiting_time exceeds self.experiment_time. - # Also, it just assumes the first event is a birth. we could also WOLOG that the first birth_waiting_time of the experiment is 0 - # (but that requires shifting times elsewhere in order to permit correct model comparison to non-ecDNA simulators. + # TO DO: this is a really hacky fix b/c it bypasses the length checks of whether + # the first birth_waiting_time exceeds self.experiment_time. + # Also, it just assumes the first event is a birth. we could also WOLOG that the + # first birth_waiting_time of the experiment is 0 + # (but that requires shifting times elsewhere in order to permit correct model + # comparison to non-ecDNA simulators). if lineage["total_time"] == 0: # Update birth rate updated_birth_scale = self.update_fitness( @@ -262,31 +285,51 @@ def sample_lineage_event( tree.add_node(unique_id) tree.nodes[unique_id]["birth_scale"] = updated_birth_scale tree.add_edge(lineage["id"], unique_id) - tree.nodes[unique_id]["time"] = birth_waiting_time + lineage["total_time"] + tree.nodes[unique_id]["time"] = ( + birth_waiting_time + lineage["total_time"] + ) tree.nodes[unique_id]["ecdna_array"] = tree.nodes[lineage["id"]][ "ecdna_array" ] # child_ecdna_array + # Add the newly generated cell to the list of living lineages - current_lineages.put( + heapq.heappush( + current_lineages, ( birth_waiting_time + lineage["total_time"], unique_id, { "id": unique_id, "birth_scale": updated_birth_scale, - "total_time": birth_waiting_time + lineage["total_time"], + "total_time": birth_waiting_time + + lineage["total_time"], "active": True, }, - ) + ), ) + # current_lineages.put( + # ( + # birth_waiting_time + lineage["total_time"], + # unique_id, + # { + # "id": unique_id, + # "birth_scale": updated_birth_scale, + # "total_time": birth_waiting_time + # + lineage["total_time"], + # "active": True, + # }, + # ) + # ) return # If birth and death would happen after the total experiment time, # just cut off the living branch length at the experiment time if ( self.experiment_time - and lineage["total_time"] + birth_waiting_time >= self.experiment_time - and lineage["total_time"] + death_waiting_time >= self.experiment_time + and lineage["total_time"] + birth_waiting_time + >= self.experiment_time + and lineage["total_time"] + death_waiting_time + >= self.experiment_time ): tree.add_node(unique_id) @@ -296,8 +339,8 @@ def sample_lineage_event( tree.nodes[unique_id]["ecdna_array"] = tree.nodes[lineage["id"]][ "ecdna_array" ] - - current_lineages.put( + heapq.heappush( + current_lineages, ( self.experiment_time, unique_id, @@ -307,8 +350,20 @@ def sample_lineage_event( "total_time": self.experiment_time, "active": False, }, - ) + ), ) + # current_lineages.put( + # ( + # self.experiment_time, + # unique_id, + # { + # "id": unique_id, + # "birth_scale": lineage["birth_scale"], + # "total_time": self.experiment_time, + # "active": False, + # }, + # ) + # ) # Indicate this node is observed at the end of experiment observed_nodes.append(unique_id) @@ -316,9 +371,9 @@ def sample_lineage_event( else: if birth_waiting_time < death_waiting_time: # Update birth rate - updated_birth_scale = self.update_fitness(tree.nodes[lineage["id"]][ - "ecdna_array" - ]) + updated_birth_scale = self.update_fitness( + tree.nodes[lineage["id"]]["ecdna_array"] + ) child_ecdna_array = self.get_ecdna_array(lineage["id"], tree) @@ -331,18 +386,33 @@ def sample_lineage_event( ) tree.nodes[unique_id]["ecdna_array"] = child_ecdna_array # Add the newly generated cell to the list of living lineages - current_lineages.put( + heapq.heappush( + current_lineages, ( birth_waiting_time + lineage["total_time"], unique_id, { "id": unique_id, "birth_scale": updated_birth_scale, - "total_time": birth_waiting_time + lineage["total_time"], + "total_time": birth_waiting_time + + lineage["total_time"], "active": True, }, - ) + ), ) + # current_lineages.put( + # ( + # birth_waiting_time + lineage["total_time"], + # unique_id, + # { + # "id": unique_id, + # "birth_scale": updated_birth_scale, + # "total_time": birth_waiting_time + # + lineage["total_time"], + # "active": True, + # }, + # ) + # ) else: tree.add_node(unique_id) @@ -351,21 +421,36 @@ def sample_lineage_event( tree.nodes[unique_id]["time"] = ( death_waiting_time + lineage["total_time"] ) - tree.nodes[unique_id]["ecdna_array"] = tree.nodes[lineage["id"]][ - "ecdna_array" - ] - current_lineages.put( + tree.nodes[unique_id]["ecdna_array"] = tree.nodes[ + lineage["id"] + ]["ecdna_array"] + heapq.heappush( + current_lineages, ( death_waiting_time + lineage["total_time"], unique_id, { "id": unique_id, "birth_scale": lineage["birth_scale"], - "total_time": death_waiting_time + lineage["total_time"], + "total_time": death_waiting_time + + lineage["total_time"], "active": False, }, - ) + ), ) + # current_lineages.put( + # ( + # death_waiting_time + lineage["total_time"], + # unique_id, + # { + # "id": unique_id, + # "birth_scale": lineage["birth_scale"], + # "total_time": death_waiting_time + # + lineage["total_time"], + # "active": False, + # }, + # ) + # ) def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: """Generates an ecDNA array for a child given its parent and sister information. @@ -393,17 +478,34 @@ def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: new_ecdna_array = np.array([0] * len(parental_ecdna_array)) - new_ecdna_array[0]= self.splitting_function(0, parental_ecdna_array[0]) + new_ecdna_array[0] = self.splitting_function( + 0, parental_ecdna_array[0] + ) for species in range(1, len(parental_ecdna_array)): - cosegregating_compartment = int(self.cosegregation_coefficient*(new_ecdna_array[0] / max(1, parental_ecdna_array[0])) * parental_ecdna_array[species]) - sister_cell_cosegregating = int(self.cosegregation_coefficient*( (parental_ecdna_array[0]-new_ecdna_array[0]) / max(1, parental_ecdna_array[0])) * parental_ecdna_array[species]) + cosegregating_compartment = int( + self.cosegregation_coefficient + * (new_ecdna_array[0] / max(1, parental_ecdna_array[0])) + * parental_ecdna_array[species] + ) + sister_cell_cosegregating = int( + self.cosegregation_coefficient + * ( + (parental_ecdna_array[0] - new_ecdna_array[0]) + / max(1, parental_ecdna_array[0]) + ) + * parental_ecdna_array[species] + ) - random_compartment = parental_ecdna_array[species] - cosegregating_compartment - sister_cell_cosegregating + random_compartment = ( + parental_ecdna_array[species] + - cosegregating_compartment + - sister_cell_cosegregating + ) inherited_fraction = self.splitting_function( - cosegregating_compartment, + cosegregating_compartment, random_compartment, ) @@ -417,24 +519,21 @@ def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: return new_ecdna_array - def populate_tree_from_simulation(self, tree: nx.DiGraph, observed_nodes: List[str]) -> CassiopeiaTree: + def populate_tree_from_simulation( + self, tree: nx.DiGraph, observed_nodes: List[str] + ) -> CassiopeiaTree: """Populates tree with appropriate meta data. - + Args: tree: The tree simulated with ecDNA and fitness values populated as attributes. observed_nodes: The observed leaves of the tree. - + Returns: A CassiopeiaTree with relevant node attributes filled in. """ cassiopeia_tree = CassiopeiaTree(tree=tree) - time_dictionary = {} - for i in tree.nodes: - time_dictionary[i] = tree.nodes[i]["time"] - cassiopeia_tree.set_times(time_dictionary) - # Prune dead lineages and collapse resulting unifurcations to_remove = list(set(cassiopeia_tree.leaves) - set(observed_nodes)) cassiopeia_tree.remove_leaves_and_prune_lineages(to_remove) @@ -445,22 +544,40 @@ def populate_tree_from_simulation(self, tree: nx.DiGraph, observed_nodes: List[s if len(cassiopeia_tree.nodes) == 1: raise ecDNABirthDeathSimulatorError( "All lineages died before stopping condition" - ) + ) + + time_dictionary = {} + for node in tree.nodes: + time_dictionary[node] = tree.nodes[node]["time"] - for node in cassiopeia_tree.depth_first_traverse_nodes(): - - cassiopeia_tree.set_attribute(node, 'ecdna_array', tree.nodes[node]['ecdna_array']) - cassiopeia_tree.set_attribute(node, 'fitness', tree.nodes[node]['birth_scale']) + cassiopeia_tree.set_attribute( + node, "ecdna_array", tree.nodes[node]["ecdna_array"] + ) + cassiopeia_tree.set_attribute( + node, "fitness", tree.nodes[node]["birth_scale"] + ) + cassiopeia_tree.set_times(time_dictionary) - cell_metadata = pd.DataFrame(columns = [f'ecDNA_{i}' for i in range(len(self.initial_copy_number))]) - - for leaf in cassiopeia_tree.leaves: - cell_metadata.loc[leaf] = cassiopeia_tree.get_attribute(leaf, 'ecdna_array') + leaf_ecdna_arrays = [ + tree.nodes[node]["ecdna_array"] for node in cassiopeia_tree.leaves + ] + cell_metadata = pd.DataFrame( + leaf_ecdna_arrays, + columns=[ + f"ecDNA_{i}" for i in range(len(self.initial_copy_number)) + ], + index=cassiopeia_tree.leaves, + ) # apply noise model for i in range(len(self.initial_copy_number)): - cell_metadata[f'Observed_ecDNA_{i}'] = cell_metadata.apply(lambda x: np.random.binomial(x[f'ecDNA_{i}'], self.capture_efficiency), axis=1) + cell_metadata[f"Observed_ecDNA_{i}"] = cell_metadata.apply( + lambda x: np.random.binomial( + x[f"ecDNA_{i}"], self.capture_efficiency + ), + axis=1, + ) cassiopeia_tree.cell_meta = cell_metadata.astype(int).copy() - return cassiopeia_tree \ No newline at end of file + return cassiopeia_tree diff --git a/test/simulator_tests/ecdna_birth_death_simulator_test.py b/test/simulator_tests/ecdna_birth_death_simulator_test.py index 1704dce4..7508b4b6 100644 --- a/test/simulator_tests/ecdna_birth_death_simulator_test.py +++ b/test/simulator_tests/ecdna_birth_death_simulator_test.py @@ -1,8 +1,9 @@ """ Tests the functionality of cassiopeia.simulator.ecDNABirthDeathSimulator. """ +import heapq from typing import List, Tuple, Generator -from queue import PriorityQueue +# from queue import PriorityQueue import unittest import networkx as nx @@ -97,7 +98,8 @@ def test_initial_sample_event(self): tree.nodes[root]["birth_scale"] = 1 tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = np.array([3, 2, 5]) - current_lineages = PriorityQueue() + # current_lineages = PriorityQueue() + current_lineages = [] observed_nodes = [] starting_lineage = { "id": root, @@ -109,9 +111,11 @@ def test_initial_sample_event(self): sim.sample_lineage_event( starting_lineage, current_lineages, tree, names, observed_nodes ) - self.assertTrue(current_lineages.qsize() == 1) + self.assertTrue(len(current_lineages) == 1) + # self.assertTrue(current_lineages.qsize() == 1) - _, _, new_lineage = current_lineages.get() + _, _, new_lineage = heapq.heappop(current_lineages) + # _, _, new_lineage = current_lineages.get() self.assertTrue( np.all( tree.nodes[root]["ecdna_array"] @@ -139,7 +143,8 @@ def test_basic_sample_lineage_events(self): tree.nodes[root]["birth_scale"] = 1 tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = np.array([3, 2, 5]) - current_lineages = PriorityQueue() + # current_lineages = PriorityQueue() + current_lineages = [] observed_nodes = [] starting_lineage = { "id": root, @@ -152,7 +157,8 @@ def test_basic_sample_lineage_events(self): starting_lineage, current_lineages, tree, names, observed_nodes ) - _, _, new_lineage = current_lineages.get() + _, _, new_lineage = heapq.heappop(current_lineages) + # _, _, new_lineage = current_lineages.get() # now, let's do one normal division sim.sample_lineage_event( new_lineage, current_lineages, tree, names, observed_nodes @@ -163,10 +169,12 @@ def test_basic_sample_lineage_events(self): new_lineage, current_lineages, tree, names, observed_nodes ) - _, _, new_lineage = current_lineages.get() + _, _, new_lineage = heapq.heappop(current_lineages) + # _, _, new_lineage = current_lineages.get() total_time_d1 = new_lineage["total_time"] - _, _, new_lineage = current_lineages.get() + _, _, new_lineage = heapq.heappop(current_lineages) + # _, _, new_lineage = current_lineages.get() total_time_d2 = new_lineage["total_time"] # This need not be true in general, it's just b/c our birth_waiting_distribution is constant. @@ -183,7 +191,8 @@ def test_basic_sample_lineage_events(self): new_lineage, current_lineages, tree, names, observed_nodes ) - _, _, new_lineage = current_lineages.get() + _, _, new_lineage = heapq.heappop(current_lineages) + # _, _, new_lineage = current_lineages.get() self.assertTrue(observed_nodes[0] == new_lineage["id"]) self.assertTrue(new_lineage["total_time"] == 5) self.assertTrue(not (new_lineage["active"])) @@ -251,7 +260,8 @@ def test_basic_cosegregation(self): tree.nodes[root]["birth_scale"] = 1 tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = np.array([5, 5]) - current_lineages = PriorityQueue() + # current_lineages = PriorityQueue() + current_lineages = [] observed_nodes = [] starting_lineage = { "id": root, @@ -265,7 +275,8 @@ def test_basic_cosegregation(self): starting_lineage, current_lineages, tree, names, observed_nodes ) - _, _, new_lineage = current_lineages.get() + _, _, new_lineage = heapq.heappop(current_lineages) + # _, _, new_lineage = current_lineages.get() # now, let's do one normal division sim.sample_lineage_event( @@ -276,8 +287,10 @@ def test_basic_cosegregation(self): ) # get out the children - _, _, child_lineage_1 = current_lineages.get() - _, _, child_lineage_2 = current_lineages.get() + _, _, child_lineage_1 = heapq.heappop(current_lineages) + _, _, child_lineage_2 = heapq.heappop(current_lineages) + # _, _, child_lineage_1 = current_lineages.get() + # _, _, child_lineage_2 = current_lineages.get() # expected arrays derivation (with random seed 41): # ecdna1 segregates as (4, 6): @@ -318,7 +331,8 @@ def test_perfect_cosegregation(self): tree.nodes[root]["birth_scale"] = 1 tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = np.array([5, 5]) - current_lineages = PriorityQueue() + # current_lineages = PriorityQueue() + current_lineages = [] observed_nodes = [] starting_lineage = { "id": root, @@ -332,7 +346,8 @@ def test_perfect_cosegregation(self): starting_lineage, current_lineages, tree, names, observed_nodes ) - _, _, new_lineage = current_lineages.get() + # _, _, new_lineage = current_lineages.get() + _, _, new_lineage = heapq.heappop(current_lineages) # now, let's do one normal division sim.sample_lineage_event( @@ -343,8 +358,10 @@ def test_perfect_cosegregation(self): new_lineage, current_lineages, tree, names, observed_nodes ) - _, _, child_lineage_1 = current_lineages.get() - _, _, child_lineage_2 = current_lineages.get() + _, _, child_lineage_1 = heapq.heappop(current_lineages) + _, _, child_lineage_2 = heapq.heappop(current_lineages) + # _, _, child_lineage_1 = current_lineages.get() + # _, _, child_lineage_2 = current_lineages.get() self.assertEqual(tree.nodes[child_lineage_1["id"]]["ecdna_array"][0], tree.nodes[child_lineage_1["id"]]["ecdna_array"][1]) self.assertEqual(tree.nodes[child_lineage_2["id"]]["ecdna_array"][0], tree.nodes[child_lineage_2["id"]]["ecdna_array"][1]) From 5ceb883ebbec554687833fe7f449bca5b45a3e2e Mon Sep 17 00:00:00 2001 From: Matthew Gregory Jones Date: Wed, 12 Apr 2023 14:43:17 -0700 Subject: [PATCH 22/37] updates for ecDNA drug perturbation simulation --- cassiopeia/data/CassiopeiaTree.py | 3 +- cassiopeia/plotting/local.py | 1 + .../simulator/BirthDeathFitnessSimulator.py | 194 +++++++----------- cassiopeia/simulator/UniformLeafSubsampler.py | 1 + .../simulator/ecDNABirthDeathSimulator.py | 133 +++++------- .../ecdna_birth_death_simulator_test.py | 15 -- 6 files changed, 138 insertions(+), 209 deletions(-) diff --git a/cassiopeia/data/CassiopeiaTree.py b/cassiopeia/data/CassiopeiaTree.py index 62f3f5fb..7bc909e0 100755 --- a/cassiopeia/data/CassiopeiaTree.py +++ b/cassiopeia/data/CassiopeiaTree.py @@ -23,6 +23,7 @@ import numpy as np import pandas as pd import scipy +import tqdm from cassiopeia.data import utilities from cassiopeia.data.Layers import Layers @@ -1580,7 +1581,7 @@ def remove_leaves_and_prune_lineages( if not self.is_leaf(n): raise CassiopeiaTreeError("A specified node is not a leaf.") - for n in nodes: + for n in tqdm.tqdm(nodes): if len(self.nodes) == 1: self.__remove_node(n) else: diff --git a/cassiopeia/plotting/local.py b/cassiopeia/plotting/local.py index 93eabb6b..e10eb3a7 100644 --- a/cassiopeia/plotting/local.py +++ b/cassiopeia/plotting/local.py @@ -171,6 +171,7 @@ def create_indel_heatmap( indel_colors: Optional[pd.DataFrame] = None, indel_priors: Optional[pd.DataFrame] = None, random_state: Optional[np.random.RandomState] = None, + clustered_linprof: Optional[pd.DataFrame] = None, ) -> Tuple[ List[ Dict[ diff --git a/cassiopeia/simulator/BirthDeathFitnessSimulator.py b/cassiopeia/simulator/BirthDeathFitnessSimulator.py index df6f00d2..a14152d2 100644 --- a/cassiopeia/simulator/BirthDeathFitnessSimulator.py +++ b/cassiopeia/simulator/BirthDeathFitnessSimulator.py @@ -8,7 +8,6 @@ import networkx as nx import numpy as np -# from queue import PriorityQueue import heapq from cassiopeia.data.CassiopeiaTree import CassiopeiaTree @@ -96,6 +95,7 @@ class BirthDeathFitnessSimulator(TreeSimulator): collapse_unifurcations: Specifies whether to collapse unifurcations in the tree resulting from pruning dead lineages random_seed: A seed for reproducibility + initial_tree: A tree used for initializing the simulation. Raises: TreeSimulatorError if invalid stopping conditions are provided or if a @@ -116,6 +116,7 @@ def __init__( experiment_time: Optional[float] = None, collapse_unifurcations: bool = True, random_seed: int = None, + initial_tree: Optional[CassiopeiaTree] = None, ): if num_extant is None and experiment_time is None: raise TreeSimulatorError( @@ -149,6 +150,9 @@ def __init__( self.collapse_unifurcations = collapse_unifurcations self.random_seed = random_seed + # useful for resuming a simulation, perhaps under different pressures. + self.initial_tree = initial_tree + def initialize_tree(self, names) -> nx.DiGraph: """initializes a tree (nx.DiGraph() object with one node). Auxiliary data for each node is grabbed from self (initial conditions / params) or hardcoded @@ -156,24 +160,74 @@ def initialize_tree(self, names) -> nx.DiGraph: Output: tree (DiGraph object with one node, the root) root (name of root node in tree) """ + if self.initial_tree: + tree = self.initial_tree.get_tree_topology() + for node in self.initial_tree.nodes: + tree.nodes[node]['birth_scale'] = self.initial_tree.get_attribute(node, 'birth_scale') + tree.nodes[node]['time'] = self.initial_tree.get_attribute(node, 'time') + return tree + tree = nx.DiGraph() root = next(names) tree.add_node(root) tree.nodes[root]["birth_scale"] = self.initial_birth_scale tree.nodes[root]["time"] = 0 - return tree, root + return tree - def make_initial_lineage_dict(self, id_value): + def make_initial_lineage_dict(self, tree: nx.DiGraph): """ uses self initial-conditions and hardcoded default parameters to create an initial lineage dict Args: id_value: name of new lineage Output: a lineage dict """ - lineage = self.make_lineage_dict( - id_value, self.initial_birth_scale, 0, True - ) + + leaves = [node for node in tree if tree.out_degree(node) == 0] + current_lineages = [] + for leaf in leaves: + + lineage = self.make_lineage_dict( + leaf, tree.nodes[leaf]['birth_scale'], tree.nodes[leaf]['time'], True + ) + + if len(tree.nodes) == 1: + return lineage + + heapq.heappush( + current_lineages, + ( + tree.nodes[leaf]['time'], + leaf, + lineage, + ), + ) + + return current_lineages + + def make_lineage_dict( + self, + id_value, + birth_scale, + total_time, + active_flag, + ): + """makes a dict (lineage) from the given parameters. keys are hardcoded. + + Args: id_value: id of new lineage + birth_scale: birth_scale parameter of new lineage + total_time: age of lineage + active_flag: bool to indicate whether lineage is active + + Returns: a dict (lineage) with the parameter values under the hard-coded keys + + """ + lineage = { + "id": id_value, + "birth_scale": birth_scale, + "total_time": total_time, + "active": active_flag, + } return lineage def simulate_tree( @@ -198,20 +252,23 @@ def simulate_tree( TreeSimulatorError if all lineages die before a stopping condition """ - def node_name_generator() -> Generator[str, None, None]: + def node_name_generator(start = 0) -> Generator[str, None, None]: """Generates unique node names for the tree.""" - i = 0 + i = start while True: yield str(i) i += 1 - names = node_name_generator() + starting_index = 0 + if self.initial_tree: + starting_index = (np.max([int(l) for l in self.initial_tree.leaves]) + 1) + names = node_name_generator(starting_index) # Set the seed if self.random_seed: np.random.seed(self.random_seed) - tree, root = self.initialize_tree(names) + tree = self.initialize_tree(names) # current_lineages = PriorityQueue() current_lineages = [] # instantiate heap @@ -229,49 +286,17 @@ def node_name_generator() -> Generator[str, None, None]: } """ - starting_lineage = self.make_initial_lineage_dict(root) - - # Sample the waiting time until the first division - self.sample_lineage_event( - starting_lineage, current_lineages, tree, names, observed_nodes - ) + starting_lineage = self.make_initial_lineage_dict(tree) + + if len(tree.nodes) == 1: + # Sample the waiting time until the first division + self.sample_lineage_event( + starting_lineage, current_lineages, tree, names, observed_nodes + ) + else: + current_lineages = starting_lineage # Perform the process until there are no active extant lineages left - # while not current_lineages.empty(): - # # If number of extant lineages is the stopping criterion, at the - # # first instance of having n extant tips, stop the experiment - # # and set the total lineage time for each lineage to be equal to - # # the minimum, to produce ultrametric trees. Also, the birth_scale - # # parameter of each leaf is rolled back to equal its parent's. - # if self.num_extant: - # if current_lineages.qsize() == self.num_extant: - # remaining_lineages = [] - # while not current_lineages.empty(): - # _, _, lineage = current_lineages.get() - # remaining_lineages.append(lineage) - # min_total_time = remaining_lineages[0]["total_time"] - # for lineage in remaining_lineages: - # parent = list(tree.predecessors(lineage["id"]))[0] - - # # I don't think this is make_daughter_node; is this some kind of a node update? - # tree.nodes[lineage["id"]]["time"] += ( - # min_total_time - lineage["total_time"] - # ) - # tree.nodes[lineage["id"]]["birth_scale"] = tree.nodes[ - # parent - # ]["birth_scale"] - # observed_nodes.append(lineage["id"]) - # break - # # Pop the minimum age lineage to simulate forward time - # _, _, lineage = current_lineages.get() - # # If the lineage is no longer active, just remove it from the queue. - # # This represents the time at which the lineage dies. - # if lineage["active"]: - # for i in range(2): - # self.sample_lineage_event( - # lineage, current_lineages, tree, names, observed_nodes - # ) - while len(current_lineages) > 0: # If number of extant lineages is the stopping criterion, at the # first instance of having n extant tips, stop the experiment @@ -288,7 +313,6 @@ def node_name_generator() -> Generator[str, None, None]: for lineage in remaining_lineages: parent = list(tree.predecessors(lineage["id"]))[0] - # I don't think this is make_daughter_node; is this some kind of a node update? tree.nodes[lineage["id"]]["time"] += ( min_total_time - lineage["total_time"] ) @@ -317,7 +341,6 @@ def sample_lineage_event( self, lineage: Dict[str, Union[int, float]], current_lineages: List[Tuple[int, Any]], - # current_lineages: PriorityQueue, tree: nx.DiGraph, names: Generator, observed_nodes: List[str], @@ -400,18 +423,6 @@ def sample_lineage_event( }, ), ) - # current_lineages.put( - # ( - # self.experiment_time, - # unique_id, - # { - # "id": unique_id, - # "birth_scale": lineage["birth_scale"], - # "total_time": self.experiment_time, - # "active": False, - # }, - # ) - # ) # Indicate this node is observed at the end of experiment observed_nodes.append(unique_id) @@ -446,19 +457,6 @@ def sample_lineage_event( ), ) - # current_lineages.put( - # ( - # birth_waiting_time + lineage["total_time"], - # unique_id, - # { - # "id": unique_id, - # "birth_scale": updated_birth_scale, - # "total_time": birth_waiting_time - # + lineage["total_time"], - # "active": True, - # }, - # ) - # ) else: tree.add_node(unique_id) @@ -482,20 +480,6 @@ def sample_lineage_event( ), ) - # current_lineages.put( - # ( - # death_waiting_time + lineage["total_time"], - # unique_id, - # { - # "id": unique_id, - # "birth_scale": lineage["birth_scale"], - # "total_time": death_waiting_time - # + lineage["total_time"], - # "active": False, - # }, - # ) - # ) - def update_fitness(self, birth_scale: float) -> float: """Updates a lineage birth scale, which represents its fitness. @@ -533,31 +517,6 @@ def update_fitness(self, birth_scale: float) -> float: ) return birth_scale * base_selection_coefficient - def make_lineage_dict( - self, - id_value, - birth_scale, - total_time, - active_flag, - ): - """makes a dict (lineage) from the given parameters. keys are hardcoded. - - Args: id_value: id of new lineage - birth_scale: birth_scale parameter of new lineage - total_time: age of lineage - active_flag: bool to indicate whether lineage is active - - Returns: a dict (lineage) with the parameter values under the hard-coded keys - - """ - lineage = { - "id": id_value, - "birth_scale": birth_scale, - "total_time": total_time, - "active": active_flag, - } - return lineage - def populate_tree_from_simulation( self, tree: nx.DiGraph, observed_nodes: List[str] ) -> CassiopeiaTree: @@ -576,6 +535,7 @@ def populate_tree_from_simulation( time_dictionary = {} for i in tree.nodes: time_dictionary[i] = tree.nodes[i]["time"] + cassiopeia_tree.set_attribute(i, 'birth_scale', tree.nodes[i]['birth_scale']) cassiopeia_tree.set_times(time_dictionary) # Prune dead lineages and collapse resulting unifurcations diff --git a/cassiopeia/simulator/UniformLeafSubsampler.py b/cassiopeia/simulator/UniformLeafSubsampler.py index f8561b46..df70f652 100644 --- a/cassiopeia/simulator/UniformLeafSubsampler.py +++ b/cassiopeia/simulator/UniformLeafSubsampler.py @@ -120,4 +120,5 @@ def subsample_leaves( [(node, tree.get_time(node)) for node in subsampled_tree.nodes] ) ) + return subsampled_tree diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 64c19ceb..aa0e4159 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -101,6 +101,9 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): stopping condition for the experiment collapse_unifurcations: Specifies whether to collapse unifurcations in the tree resulting from pruning dead lineages + prune_dead_lineages: Whether or not to prune dead (unobserved) lineages. + Can be more efficient to not prune, and instead compute statistics + on living lineages downstream. random_seed: A seed for reproducibility initial_copy_number: Initial copy number for parental lineage. cosegregation_coefficient: A coefficient describing how likely it is for one species to be co-inherited @@ -110,6 +113,8 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): TO DO: fix this implementation to allow for non-independent segregation. fitness_array: Fitnesses with respect to copy number of each species in a cell. This should be a matrix in R^e (where e is the number of ecDNA species being modelled). + fitness_function: A function that produces a fitness value as a function of copy number and the selection coefficient + encoded by the fitness array. capture_efficiency: Probability of observing an ecDNA species. Used as the the probability of a binomial process. Raises: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) @@ -131,13 +136,16 @@ def __init__( num_extant: Optional[int] = None, experiment_time: Optional[float] = None, collapse_unifurcations: bool = True, + prune_dead_lineages: bool = True, random_seed: int = None, initial_copy_number: np.array = np.array([1]), cosegregation_coefficient: float = 0.0, splitting_function: Callable[[int], int] = lambda c, x: c + np.random.binomial(x, p=0.5), fitness_array: np.array = np.array([0, 1]), + fitness_function: Optional[Callable[[int, int, float], float]] = None, capture_efficiency: float = 1.0, + initial_tree: Optional[CassiopeiaTree] = None, ): if num_extant is None and experiment_time is None: raise TreeSimulatorError( @@ -169,16 +177,28 @@ def __init__( self.num_extant = num_extant self.experiment_time = experiment_time self.collapse_unifurcations = collapse_unifurcations + self.prune_dead_lineages = prune_dead_lineages self.random_seed = random_seed self.initial_copy_number = initial_copy_number self.cosegregation_coefficient = cosegregation_coefficient self.splitting_function = splitting_function self.fitness_array = fitness_array + self.fitness_function = fitness_function self.capture_efficiency = capture_efficiency + self.initial_tree = initial_tree # update to store cn_array in node. (tree.nodes[root]["cn_array"]) def initialize_tree(self, names) -> nx.DiGraph: """initializes a tree (nx.DiGraph() object with one node)""" + + if self.initial_tree: + tree = self.initial_tree.get_tree_topology() + for node in self.initial_tree.nodes: + tree.nodes[node]['birth_scale'] = self.update_fitness(self.initial_tree.get_attribute(node, 'ecdna_array')) + tree.nodes[node]['time'] = self.initial_tree.get_attribute(node, 'time') + tree.nodes[node]['ecdna_array'] = self.initial_tree.get_attribute(node, 'ecdna_array') + return tree + tree = nx.DiGraph() root = next(names) tree.add_node(root) @@ -186,7 +206,7 @@ def initialize_tree(self, names) -> nx.DiGraph: tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = self.initial_copy_number - return tree, root + return tree # update to compute fitness using lineage cn_array def update_fitness(self, ecdna_array: np.array) -> float: @@ -205,14 +225,18 @@ def update_fitness(self, ecdna_array: np.array) -> float: TreeSimulatorError if a negative number of mutations is sampled """ - return self.initial_birth_scale * ( - 1.0 + self.fitness_array[tuple((ecdna_array > 0).astype(int))] - ) + if self.fitness_function is None: + return self.initial_birth_scale * ( + 1.0 + self.fitness_array[tuple((ecdna_array > 0).astype(int))] + ) + else: + return self.initial_birth_scale * ( + 1.0 + self.fitness_function(ecdna_array[0], ecdna_array[1], self.fitness_array[tuple((ecdna_array > 0).astype(int))]) + ) def sample_lineage_event( self, lineage: Dict[str, Union[int, float]], - # current_lineages: PriorityQueue, current_lineages: List[Tuple[int, Any]], tree: nx.DiGraph, names: Generator, @@ -307,19 +331,7 @@ def sample_lineage_event( }, ), ) - # current_lineages.put( - # ( - # birth_waiting_time + lineage["total_time"], - # unique_id, - # { - # "id": unique_id, - # "birth_scale": updated_birth_scale, - # "total_time": birth_waiting_time - # + lineage["total_time"], - # "active": True, - # }, - # ) - # ) + return # If birth and death would happen after the total experiment time, @@ -352,19 +364,7 @@ def sample_lineage_event( }, ), ) - # current_lineages.put( - # ( - # self.experiment_time, - # unique_id, - # { - # "id": unique_id, - # "birth_scale": lineage["birth_scale"], - # "total_time": self.experiment_time, - # "active": False, - # }, - # ) - # ) - + # Indicate this node is observed at the end of experiment observed_nodes.append(unique_id) @@ -400,19 +400,6 @@ def sample_lineage_event( }, ), ) - # current_lineages.put( - # ( - # birth_waiting_time + lineage["total_time"], - # unique_id, - # { - # "id": unique_id, - # "birth_scale": updated_birth_scale, - # "total_time": birth_waiting_time - # + lineage["total_time"], - # "active": True, - # }, - # ) - # ) else: tree.add_node(unique_id) @@ -438,19 +425,6 @@ def sample_lineage_event( }, ), ) - # current_lineages.put( - # ( - # death_waiting_time + lineage["total_time"], - # unique_id, - # { - # "id": unique_id, - # "birth_scale": lineage["birth_scale"], - # "total_time": death_waiting_time - # + lineage["total_time"], - # "active": False, - # }, - # ) - # ) def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: """Generates an ecDNA array for a child given its parent and sister information. @@ -534,30 +508,29 @@ def populate_tree_from_simulation( cassiopeia_tree = CassiopeiaTree(tree=tree) - # Prune dead lineages and collapse resulting unifurcations - to_remove = list(set(cassiopeia_tree.leaves) - set(observed_nodes)) - cassiopeia_tree.remove_leaves_and_prune_lineages(to_remove) - if self.collapse_unifurcations and len(cassiopeia_tree.nodes) > 1: - cassiopeia_tree.collapse_unifurcations(source="1") - - # If only implicit root remains after pruning dead lineages, error - if len(cassiopeia_tree.nodes) == 1: - raise ecDNABirthDeathSimulatorError( - "All lineages died before stopping condition" - ) - + # transfer over all meta data time_dictionary = {} for node in tree.nodes: - time_dictionary[node] = tree.nodes[node]["time"] - cassiopeia_tree.set_attribute( - node, "ecdna_array", tree.nodes[node]["ecdna_array"] - ) - cassiopeia_tree.set_attribute( - node, "fitness", tree.nodes[node]["birth_scale"] - ) + time_dictionary[node] = tree.nodes[node]["time"] + + cassiopeia_tree.set_attribute( + node, "ecdna_array", tree.nodes[node]["ecdna_array"] + ) + cassiopeia_tree.set_attribute( + node, "fitness", tree.nodes[node]["birth_scale"] + ) + cassiopeia_tree.set_times(time_dictionary) + # Prune dead lineages and collapse resulting unifurcations + to_remove = (set(cassiopeia_tree.leaves) - set(observed_nodes)) + if self.prune_dead_lineages: + print(f'Removing {len(to_remove)} sublineages.') + cassiopeia_tree.remove_leaves_and_prune_lineages(list(to_remove)) + if self.collapse_unifurcations and len(cassiopeia_tree.nodes) > 1: + cassiopeia_tree.collapse_unifurcations(source="1") + leaf_ecdna_arrays = [ tree.nodes[node]["ecdna_array"] for node in cassiopeia_tree.leaves ] @@ -580,4 +553,12 @@ def populate_tree_from_simulation( cassiopeia_tree.cell_meta = cell_metadata.astype(int).copy() + cassiopeia_tree.cell_meta['Observed'] = ['False' if n in to_remove else 'True' for n in cassiopeia_tree.leaves] + + # If only implicit root remains after pruning dead lineages, error + if len(cassiopeia_tree.nodes) == 1: + raise ecDNABirthDeathSimulatorError( + "All lineages died before stopping condition" + ) + return cassiopeia_tree diff --git a/test/simulator_tests/ecdna_birth_death_simulator_test.py b/test/simulator_tests/ecdna_birth_death_simulator_test.py index 7508b4b6..5a697961 100644 --- a/test/simulator_tests/ecdna_birth_death_simulator_test.py +++ b/test/simulator_tests/ecdna_birth_death_simulator_test.py @@ -112,10 +112,8 @@ def test_initial_sample_event(self): starting_lineage, current_lineages, tree, names, observed_nodes ) self.assertTrue(len(current_lineages) == 1) - # self.assertTrue(current_lineages.qsize() == 1) _, _, new_lineage = heapq.heappop(current_lineages) - # _, _, new_lineage = current_lineages.get() self.assertTrue( np.all( tree.nodes[root]["ecdna_array"] @@ -143,7 +141,6 @@ def test_basic_sample_lineage_events(self): tree.nodes[root]["birth_scale"] = 1 tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = np.array([3, 2, 5]) - # current_lineages = PriorityQueue() current_lineages = [] observed_nodes = [] starting_lineage = { @@ -158,7 +155,6 @@ def test_basic_sample_lineage_events(self): ) _, _, new_lineage = heapq.heappop(current_lineages) - # _, _, new_lineage = current_lineages.get() # now, let's do one normal division sim.sample_lineage_event( new_lineage, current_lineages, tree, names, observed_nodes @@ -170,11 +166,9 @@ def test_basic_sample_lineage_events(self): ) _, _, new_lineage = heapq.heappop(current_lineages) - # _, _, new_lineage = current_lineages.get() total_time_d1 = new_lineage["total_time"] _, _, new_lineage = heapq.heappop(current_lineages) - # _, _, new_lineage = current_lineages.get() total_time_d2 = new_lineage["total_time"] # This need not be true in general, it's just b/c our birth_waiting_distribution is constant. @@ -192,7 +186,6 @@ def test_basic_sample_lineage_events(self): ) _, _, new_lineage = heapq.heappop(current_lineages) - # _, _, new_lineage = current_lineages.get() self.assertTrue(observed_nodes[0] == new_lineage["id"]) self.assertTrue(new_lineage["total_time"] == 5) self.assertTrue(not (new_lineage["active"])) @@ -260,7 +253,6 @@ def test_basic_cosegregation(self): tree.nodes[root]["birth_scale"] = 1 tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = np.array([5, 5]) - # current_lineages = PriorityQueue() current_lineages = [] observed_nodes = [] starting_lineage = { @@ -276,7 +268,6 @@ def test_basic_cosegregation(self): ) _, _, new_lineage = heapq.heappop(current_lineages) - # _, _, new_lineage = current_lineages.get() # now, let's do one normal division sim.sample_lineage_event( @@ -289,8 +280,6 @@ def test_basic_cosegregation(self): # get out the children _, _, child_lineage_1 = heapq.heappop(current_lineages) _, _, child_lineage_2 = heapq.heappop(current_lineages) - # _, _, child_lineage_1 = current_lineages.get() - # _, _, child_lineage_2 = current_lineages.get() # expected arrays derivation (with random seed 41): # ecdna1 segregates as (4, 6): @@ -331,7 +320,6 @@ def test_perfect_cosegregation(self): tree.nodes[root]["birth_scale"] = 1 tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = np.array([5, 5]) - # current_lineages = PriorityQueue() current_lineages = [] observed_nodes = [] starting_lineage = { @@ -346,7 +334,6 @@ def test_perfect_cosegregation(self): starting_lineage, current_lineages, tree, names, observed_nodes ) - # _, _, new_lineage = current_lineages.get() _, _, new_lineage = heapq.heappop(current_lineages) # now, let's do one normal division @@ -360,8 +347,6 @@ def test_perfect_cosegregation(self): _, _, child_lineage_1 = heapq.heappop(current_lineages) _, _, child_lineage_2 = heapq.heappop(current_lineages) - # _, _, child_lineage_1 = current_lineages.get() - # _, _, child_lineage_2 = current_lineages.get() self.assertEqual(tree.nodes[child_lineage_1["id"]]["ecdna_array"][0], tree.nodes[child_lineage_1["id"]]["ecdna_array"][1]) self.assertEqual(tree.nodes[child_lineage_2["id"]]["ecdna_array"][0], tree.nodes[child_lineage_2["id"]]["ecdna_array"][1]) From f32bfe0f872b78ab5ebe384833540f55f95b5d38 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Fri, 12 Apr 2024 14:12:26 -0700 Subject: [PATCH 23/37] updated format --- .../simulator/ecDNABirthDeathSimulator.py | 117 +++++++++++------- 1 file changed, 70 insertions(+), 47 deletions(-) diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index aa0e4159..c2751b32 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -3,6 +3,7 @@ process, including differing fitness on lineages on the tree. Allows for a variety of division and fitness regimes to be specified by the user. """ + from random import random from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union @@ -50,9 +51,7 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): these two stopping criteria must be provided. Both can be provided in which case the simulation is run until one of the stopping conditions is reached." - -(excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) - - Example use snippet: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) + Example use snippet: # note that numpy uses a different parameterization of the # exponential distribution with the scale parameter, which is 1/rate @@ -74,14 +73,15 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): ) tree = bd_sim.simulate_tree() - Args: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) + Args: birth_waiting_distribution: A function that samples waiting times from the birth distribution. Determines how often births occur. Must take a scale parameter as the input initial_birth_scale: The initial scale parameter that is used at the start of the experiment death_waiting_distribution: A function that samples waiting times - from the death distribution. Determines how often deaths occur. Default is no-death. + from the death distribution. Determines how often deaths occur. + Default is no-death. mutation_distribution: A function that samples the number of mutations that occur at a division event. If None, then no mutations are sampled @@ -106,18 +106,25 @@ class ecDNABirthDeathSimulator(BirthDeathFitnessSimulator): on living lineages downstream. random_seed: A seed for reproducibility initial_copy_number: Initial copy number for parental lineage. - cosegregation_coefficient: A coefficient describing how likely it is for one species to be co-inherited - with one specific species (currently modeled as the first in the array). - TODO: how do we make this generalizable to multiple species each with different pairwise covariances? - splitting_function: As implemented, the function that describes segregation of each species at cell division. - TO DO: fix this implementation to allow for non-independent segregation. - fitness_array: Fitnesses with respect to copy number of each species in a cell. This should be a matrix in - R^e (where e is the number of ecDNA species being modelled). - fitness_function: A function that produces a fitness value as a function of copy number and the selection coefficient - encoded by the fitness array. - capture_efficiency: Probability of observing an ecDNA species. Used as the the probability of a binomial process. - - Raises: (excerpted from from BirthDeathFitnessSimulator, update / check for accuracy) + cosegregation_coefficient: A coefficient describing how likely it is for + one species to be co-inherited with one specific species (currently + modeled as the first in the array). + TODO: how do we make this generalizable to multiple species each + with different pairwise covariances? + splitting_function: As implemented, the function that describes + segregation of each species at cell division. + TODO: fix this implementation to allow for non-independent + segregation. + fitness_array: Fitnesses with respect to copy number of each species in + a cell. This should be a matrix in R^e (where e is the number of + ecDNA species being modelled). + fitness_function: A function that produces a fitness value as a function + of copy number and the selection coefficient encoded by the fitness + array. + capture_efficiency: Probability of observing an ecDNA species. Used as + the the probability of a binomial process. + + Raises: TreeSimulatorError if invalid stopping conditions are provided or if a fitness distribution is not provided when a mutation distribution isn't """ @@ -194,9 +201,15 @@ def initialize_tree(self, names) -> nx.DiGraph: if self.initial_tree: tree = self.initial_tree.get_tree_topology() for node in self.initial_tree.nodes: - tree.nodes[node]['birth_scale'] = self.update_fitness(self.initial_tree.get_attribute(node, 'ecdna_array')) - tree.nodes[node]['time'] = self.initial_tree.get_attribute(node, 'time') - tree.nodes[node]['ecdna_array'] = self.initial_tree.get_attribute(node, 'ecdna_array') + tree.nodes[node]["birth_scale"] = self.update_fitness( + self.initial_tree.get_attribute(node, "ecdna_array") + ) + tree.nodes[node]["time"] = self.initial_tree.get_attribute( + node, "time" + ) + tree.nodes[node]["ecdna_array"] = ( + self.initial_tree.get_attribute(node, "ecdna_array") + ) return tree tree = nx.DiGraph() @@ -210,10 +223,11 @@ def initialize_tree(self, names) -> nx.DiGraph: # update to compute fitness using lineage cn_array def update_fitness(self, ecdna_array: np.array) -> float: - """Updates a lineage birth scale, which represents its (Malthusian) fitness. + """Updates a lineage birth scale, representing its (Malthusian) fitness. - Fitness is computed as a function of copy number, using the fitness_array - (which defines fitness for CN=0 or CN >0 for each species, with epistasis). + Fitness is computed as a function of copy number, using the + fitness_array (which defines fitness for CN=0 or CN >0 for each species, + with epistasis). Args: ecdna_array: The birth_scale to be updated @@ -231,7 +245,12 @@ def update_fitness(self, ecdna_array: np.array) -> float: ) else: return self.initial_birth_scale * ( - 1.0 + self.fitness_function(ecdna_array[0], ecdna_array[1], self.fitness_array[tuple((ecdna_array > 0).astype(int))]) + 1.0 + + self.fitness_function( + ecdna_array[0], + ecdna_array[1], + self.fitness_array[tuple((ecdna_array > 0).astype(int))], + ) ) def sample_lineage_event( @@ -293,12 +312,12 @@ def sample_lineage_event( if birth_waiting_time <= 0 or death_waiting_time <= 0: raise TreeSimulatorError("0 or negative waiting time detected") - # TO DO: this is a really hacky fix b/c it bypasses the length checks of whether - # the first birth_waiting_time exceeds self.experiment_time. - # Also, it just assumes the first event is a birth. we could also WOLOG that the - # first birth_waiting_time of the experiment is 0 - # (but that requires shifting times elsewhere in order to permit correct model - # comparison to non-ecDNA simulators). + # TO DO: this is a really hacky fix b/c it bypasses the length checks of + # whether the first birth_waiting_time exceeds self.experiment_time. + # Also, it just assumes the first event is a birth. we could also + # WOLOG that the first birth_waiting_time of the experiment is 0 + # (but that requires shifting times elsewhere in order to permit correct + # model comparison to non-ecDNA simulators). if lineage["total_time"] == 0: # Update birth rate updated_birth_scale = self.update_fitness( @@ -331,7 +350,7 @@ def sample_lineage_event( }, ), ) - + return # If birth and death would happen after the total experiment time, @@ -364,7 +383,7 @@ def sample_lineage_event( }, ), ) - + # Indicate this node is observed at the end of experiment observed_nodes.append(unique_id) @@ -427,14 +446,14 @@ def sample_lineage_event( ) def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: - """Generates an ecDNA array for a child given its parent and sister information. + """Generates an ecDNA array for a child given its parent and sisters. Args: parent_id: ID of parent in the generated tree. tree: The in-progress tree. Returns: - Numpy array corresponding to the ecDNA copy numbers for the new child. + Numpy array corresponding to the ecDNA copy numbers for the child. """ parental_ecdna_array = 2 * tree.nodes[parent_id]["ecdna_array"] @@ -499,7 +518,8 @@ def populate_tree_from_simulation( """Populates tree with appropriate meta data. Args: - tree: The tree simulated with ecDNA and fitness values populated as attributes. + tree: The tree simulated with ecDNA and fitness values populated + as attributes. observed_nodes: The observed leaves of the tree. Returns: @@ -512,21 +532,21 @@ def populate_tree_from_simulation( time_dictionary = {} for node in tree.nodes: - time_dictionary[node] = tree.nodes[node]["time"] - - cassiopeia_tree.set_attribute( - node, "ecdna_array", tree.nodes[node]["ecdna_array"] - ) - cassiopeia_tree.set_attribute( - node, "fitness", tree.nodes[node]["birth_scale"] - ) + time_dictionary[node] = tree.nodes[node]["time"] + + cassiopeia_tree.set_attribute( + node, "ecdna_array", tree.nodes[node]["ecdna_array"] + ) + cassiopeia_tree.set_attribute( + node, "fitness", tree.nodes[node]["birth_scale"] + ) cassiopeia_tree.set_times(time_dictionary) - # Prune dead lineages and collapse resulting unifurcations - to_remove = (set(cassiopeia_tree.leaves) - set(observed_nodes)) + # Prune dead lineages and collapse resulting unifurcations + to_remove = set(cassiopeia_tree.leaves) - set(observed_nodes) if self.prune_dead_lineages: - print(f'Removing {len(to_remove)} sublineages.') + print(f"Removing {len(to_remove)} sublineages.") cassiopeia_tree.remove_leaves_and_prune_lineages(list(to_remove)) if self.collapse_unifurcations and len(cassiopeia_tree.nodes) > 1: cassiopeia_tree.collapse_unifurcations(source="1") @@ -553,7 +573,10 @@ def populate_tree_from_simulation( cassiopeia_tree.cell_meta = cell_metadata.astype(int).copy() - cassiopeia_tree.cell_meta['Observed'] = ['False' if n in to_remove else 'True' for n in cassiopeia_tree.leaves] + cassiopeia_tree.cell_meta["Observed"] = [ + "False" if n in to_remove else "True" + for n in cassiopeia_tree.leaves + ] # If only implicit root remains after pruning dead lineages, error if len(cassiopeia_tree.nodes) == 1: From a476f12d27f1b7c33d5eccfb2f19cb88ee92bf93 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Sat, 13 Apr 2024 15:04:47 -0700 Subject: [PATCH 24/37] fixed tests --- .../simulator/ecDNABirthDeathSimulator.py | 2 +- .../ecdna_birth_death_simulator_test.py | 134 ++++++++++++------ 2 files changed, 93 insertions(+), 43 deletions(-) diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index c2751b32..4536294f 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -545,7 +545,7 @@ def populate_tree_from_simulation( # Prune dead lineages and collapse resulting unifurcations to_remove = set(cassiopeia_tree.leaves) - set(observed_nodes) - if self.prune_dead_lineages: + if self.prune_dead_lineages and len(to_remove) > 0: print(f"Removing {len(to_remove)} sublineages.") cassiopeia_tree.remove_leaves_and_prune_lineages(list(to_remove)) if self.collapse_unifurcations and len(cassiopeia_tree.nodes) > 1: diff --git a/test/simulator_tests/ecdna_birth_death_simulator_test.py b/test/simulator_tests/ecdna_birth_death_simulator_test.py index 5a697961..eba7d8fe 100644 --- a/test/simulator_tests/ecdna_birth_death_simulator_test.py +++ b/test/simulator_tests/ecdna_birth_death_simulator_test.py @@ -1,8 +1,10 @@ """ Tests the functionality of cassiopeia.simulator.ecDNABirthDeathSimulator. """ + import heapq from typing import List, Tuple, Generator + # from queue import PriorityQueue import unittest @@ -24,6 +26,7 @@ def node_name_generator() -> Generator[str, None, None]: yield str(i) i += 1 + def extract_tree_statistics( tree: CassiopeiaTree, ) -> Tuple[List[float], int, bool]: @@ -77,7 +80,7 @@ def test_ecdna_splitting(self): new_ecdna_array = sim.get_ecdna_array(0, tree) expected_array = [3, 3] # calculated manually as [4*2-5, 5*2 - 7] self.assertTrue(np.all(expected_array == new_ecdna_array)) - + def test_initial_sample_event(self): np.random.seed(41) @@ -86,7 +89,9 @@ def test_initial_sample_event(self): initial_birth_scale=1, num_extant=16, experiment_time=5, - fitness_array = np.array([[[0,0.1], [0.1,0.2]],[[0.1,0.5],[0.2,0.6]]]), + fitness_array=np.array( + [[[0, 0.1], [0.1, 0.2]], [[0.1, 0.5], [0.2, 0.6]]] + ), ) # first, we'll trigger the time=0 condition in sample_lineage_event. @@ -130,7 +135,9 @@ def test_basic_sample_lineage_events(self): initial_birth_scale=1, num_extant=16, experiment_time=5, - fitness_array = np.array([[[0,0.1], [0.1,0.2]],[[0.1,0.5],[0.2,0.6]]]), + fitness_array=np.array( + [[[0, 0.1], [0.1, 0.2]], [[0.1, 0.5], [0.2, 0.6]]] + ), ) names = node_name_generator() @@ -176,7 +183,9 @@ def test_basic_sample_lineage_events(self): expected_array = [4, 4, 4] self.assertTrue( - np.all(tree.nodes[new_lineage["id"]]["ecdna_array"] == expected_array) + np.all( + tree.nodes[new_lineage["id"]]["ecdna_array"] == expected_array + ) ) # Now, let's edit new_lineage to ensure it triggers stopping condition, then pass it into sample_lineage_event @@ -198,9 +207,9 @@ def test_populate_tree_from_simulation(self): initial_birth_scale=1, num_extant=16, experiment_time=5, - initial_copy_number=[3,2,5] + initial_copy_number=[3, 2, 5], ) - + names = node_name_generator() tree = nx.DiGraph() @@ -214,22 +223,35 @@ def test_populate_tree_from_simulation(self): tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = np.array([3, 2, 5]) - tree.nodes[child_1]['birth_scale'] = 1 + tree.nodes[child_1]["birth_scale"] = 1 tree.nodes[child_1]["time"] = 2 - tree.nodes[child_1]["ecdna_array"] = np.array([2,0,6]) - - tree.nodes[child_2]['birth_scale'] = 1 + tree.nodes[child_1]["ecdna_array"] = np.array([2, 0, 6]) + + tree.nodes[child_2]["birth_scale"] = 1 tree.nodes[child_2]["time"] = 2 tree.nodes[child_2]["ecdna_array"] = np.array([4, 4, 4]) - cassiopeia_tree = sim.populate_tree_from_simulation(tree, [child_1, child_2]) + cassiopeia_tree = sim.populate_tree_from_simulation( + tree, [child_1, child_2] + ) - expected_meta_data = pd.DataFrame.from_dict({ - child_1: [2, 0, 6, 2, 0, 6], - child_2: [4, 4, 4, 4, 4, 4] - }, orient='index', columns = ['ecDNA_0', 'ecDNA_1', 'ecDNA_2', 'Observed_ecDNA_0', 'Observed_ecDNA_1', 'Observed_ecDNA_2']) - - pd.testing.assert_frame_equal(expected_meta_data, cassiopeia_tree.cell_meta.astype(int)) + expected_meta_data = pd.DataFrame.from_dict( + {child_1: [2, 0, 6, 2, 0, 6], child_2: [4, 4, 4, 4, 4, 4]}, + orient="index", + columns=[ + "ecDNA_0", + "ecDNA_1", + "ecDNA_2", + "Observed_ecDNA_0", + "Observed_ecDNA_1", + "Observed_ecDNA_2", + ], + ) + + pd.testing.assert_frame_equal( + expected_meta_data, + cassiopeia_tree.cell_meta[expected_meta_data.columns].astype(int), + ) def test_basic_cosegregation(self): @@ -242,7 +264,7 @@ def test_basic_cosegregation(self): num_extant=16, experiment_time=5, cosegregation_coefficient=0.5, - fitness_array = np.array([[0, 0.5], [0.5, 0]]), + fitness_array=np.array([[0, 0.5], [0.5, 0]]), ) names = node_name_generator() @@ -262,7 +284,7 @@ def test_basic_cosegregation(self): "active": True, } - # simulate time till first cell division + # simulate time till first cell division sim.sample_lineage_event( starting_lineage, current_lineages, tree, names, observed_nodes ) @@ -280,7 +302,7 @@ def test_basic_cosegregation(self): # get out the children _, _, child_lineage_1 = heapq.heappop(current_lineages) _, _, child_lineage_2 = heapq.heappop(current_lineages) - + # expected arrays derivation (with random seed 41): # ecdna1 segregates as (4, 6): # ecdna2 co-segregation compartment - (2, 3) @@ -292,11 +314,17 @@ def test_basic_cosegregation(self): print(tree.nodes[child_lineage_1["id"]]["ecdna_array"]) self.assertTrue( - np.all(tree.nodes[child_lineage_1["id"]]["ecdna_array"] == expected_child_1_array) + np.all( + tree.nodes[child_lineage_1["id"]]["ecdna_array"] + == expected_child_1_array + ) ) self.assertTrue( - np.all(tree.nodes[child_lineage_2["id"]]["ecdna_array"] == expected_child_2_array) - ) + np.all( + tree.nodes[child_lineage_2["id"]]["ecdna_array"] + == expected_child_2_array + ) + ) def test_perfect_cosegregation(self): @@ -309,7 +337,7 @@ def test_perfect_cosegregation(self): num_extant=16, experiment_time=5, cosegregation_coefficient=1.0, - fitness_array = np.array([[0, 0.5], [0.5, 0]]), + fitness_array=np.array([[0, 0.5], [0.5, 0]]), ) names = node_name_generator() @@ -329,7 +357,7 @@ def test_perfect_cosegregation(self): "active": True, } - # simulate time till first cell division + # simulate time till first cell division sim.sample_lineage_event( starting_lineage, current_lineages, tree, names, observed_nodes ) @@ -348,8 +376,14 @@ def test_perfect_cosegregation(self): _, _, child_lineage_1 = heapq.heappop(current_lineages) _, _, child_lineage_2 = heapq.heappop(current_lineages) - self.assertEqual(tree.nodes[child_lineage_1["id"]]["ecdna_array"][0], tree.nodes[child_lineage_1["id"]]["ecdna_array"][1]) - self.assertEqual(tree.nodes[child_lineage_2["id"]]["ecdna_array"][0], tree.nodes[child_lineage_2["id"]]["ecdna_array"][1]) + self.assertEqual( + tree.nodes[child_lineage_1["id"]]["ecdna_array"][0], + tree.nodes[child_lineage_1["id"]]["ecdna_array"][1], + ) + self.assertEqual( + tree.nodes[child_lineage_2["id"]]["ecdna_array"][0], + tree.nodes[child_lineage_2["id"]]["ecdna_array"][1], + ) def test_low_capture_efficiency(self): @@ -359,10 +393,10 @@ def test_low_capture_efficiency(self): initial_birth_scale=1, num_extant=16, experiment_time=5, - initial_copy_number=[3,2,5], + initial_copy_number=[3, 2, 5], capture_efficiency=0.5, ) - + names = node_name_generator() tree = nx.DiGraph() @@ -376,16 +410,18 @@ def test_low_capture_efficiency(self): tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = np.array([3, 2, 5]) - tree.nodes[child_1]['birth_scale'] = 1 + tree.nodes[child_1]["birth_scale"] = 1 tree.nodes[child_1]["time"] = 2 - tree.nodes[child_1]["ecdna_array"] = np.array([2,0,6]) - - tree.nodes[child_2]['birth_scale'] = 1 + tree.nodes[child_1]["ecdna_array"] = np.array([2, 0, 6]) + + tree.nodes[child_2]["birth_scale"] = 1 tree.nodes[child_2]["time"] = 2 tree.nodes[child_2]["ecdna_array"] = np.array([4, 4, 4]) - cassiopeia_tree = sim.populate_tree_from_simulation(tree, [child_1, child_2]) - + cassiopeia_tree = sim.populate_tree_from_simulation( + tree, [child_1, child_2] + ) + # reset seed to create expected observations np.random.seed(41) c1_0 = np.random.binomial(2, 0.5) @@ -395,13 +431,27 @@ def test_low_capture_efficiency(self): c1_2 = np.random.binomial(6, 0.5) c2_2 = np.random.binomial(4, 0.5) - expected_meta_data = pd.DataFrame.from_dict({ - child_1: [2, 0, 6, c1_0, c1_1, c1_2], - child_2: [4, 4, 4, c2_0, c2_1, c2_2] - }, orient='index', columns = ['ecDNA_0', 'ecDNA_1', 'ecDNA_2', 'Observed_ecDNA_0', 'Observed_ecDNA_1', 'Observed_ecDNA_2']) - - pd.testing.assert_frame_equal(expected_meta_data, cassiopeia_tree.cell_meta.astype(int)) + expected_meta_data = pd.DataFrame.from_dict( + { + child_1: [2, 0, 6, c1_0, c1_1, c1_2], + child_2: [4, 4, 4, c2_0, c2_1, c2_2], + }, + orient="index", + columns=[ + "ecDNA_0", + "ecDNA_1", + "ecDNA_2", + "Observed_ecDNA_0", + "Observed_ecDNA_1", + "Observed_ecDNA_2", + ], + ) + + pd.testing.assert_frame_equal( + expected_meta_data, + cassiopeia_tree.cell_meta[expected_meta_data.columns].astype(int), + ) + - if __name__ == "__main__": unittest.main() From 7d63c8ea90d285c63bc3acedfc5dc32a3f440a89 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Sat, 13 Apr 2024 15:27:36 -0700 Subject: [PATCH 25/37] reverted back to PriorityQueue --- cassiopeia/data/CassiopeiaTree.py | 1 - .../simulator/BirthDeathFitnessSimulator.py | 73 ++++++++----------- 2 files changed, 30 insertions(+), 44 deletions(-) diff --git a/cassiopeia/data/CassiopeiaTree.py b/cassiopeia/data/CassiopeiaTree.py index 59188418..482a4bbe 100755 --- a/cassiopeia/data/CassiopeiaTree.py +++ b/cassiopeia/data/CassiopeiaTree.py @@ -24,7 +24,6 @@ import numpy as np import pandas as pd import scipy -import tqdm from cassiopeia.data import utilities from cassiopeia.data.Layers import Layers diff --git a/cassiopeia/simulator/BirthDeathFitnessSimulator.py b/cassiopeia/simulator/BirthDeathFitnessSimulator.py index a14152d2..bcd69d77 100644 --- a/cassiopeia/simulator/BirthDeathFitnessSimulator.py +++ b/cassiopeia/simulator/BirthDeathFitnessSimulator.py @@ -3,12 +3,11 @@ process, including differing fitness on lineages on the tree. Allows for a variety of division and fitness regimes to be specified by the user. """ -from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union +from typing import Callable, Dict, Generator, List, Optional, Union import networkx as nx import numpy as np - -import heapq +from queue import PriorityQueue from cassiopeia.data.CassiopeiaTree import CassiopeiaTree from cassiopeia.mixins import TreeSimulatorError @@ -184,24 +183,23 @@ def make_initial_lineage_dict(self, tree: nx.DiGraph): """ leaves = [node for node in tree if tree.out_degree(node) == 0] - current_lineages = [] + current_lineages = PriorityQueue() for leaf in leaves: - lineage = self.make_lineage_dict( + lineage_dict = self.make_lineage_dict( leaf, tree.nodes[leaf]['birth_scale'], tree.nodes[leaf]['time'], True ) if len(tree.nodes) == 1: - return lineage - - heapq.heappush( - current_lineages, - ( - tree.nodes[leaf]['time'], - leaf, - lineage, - ), + return lineage_dict + + current_lineages.put( + ( + tree.nodes[leaf]['time'], + leaf, + lineage_dict ) + ) return current_lineages @@ -222,13 +220,13 @@ def make_lineage_dict( Returns: a dict (lineage) with the parameter values under the hard-coded keys """ - lineage = { + lineage_dict = { "id": id_value, "birth_scale": birth_scale, "total_time": total_time, "active": active_flag, } - return lineage + return lineage_dict def simulate_tree( self, @@ -267,25 +265,15 @@ def node_name_generator(start = 0) -> Generator[str, None, None]: # Set the seed if self.random_seed: np.random.seed(self.random_seed) - + tree = self.initialize_tree(names) - # current_lineages = PriorityQueue() - current_lineages = [] # instantiate heap + current_lineages = PriorityQueue() # instantiate queue # Records the nodes that are observed at the end of the experiment # TO DO: update to accept arbitrary fields in the dict. observed_nodes = [] - """ - starting_lineage = { - "id": root, - "birth_scale": self.initial_birth_scale, - "total_time": 0, - "active": True, - } - """ - starting_lineage = self.make_initial_lineage_dict(tree) if len(tree.nodes) == 1: @@ -297,17 +285,17 @@ def node_name_generator(start = 0) -> Generator[str, None, None]: current_lineages = starting_lineage # Perform the process until there are no active extant lineages left - while len(current_lineages) > 0: + while not current_lineages.empty(): # If number of extant lineages is the stopping criterion, at the # first instance of having n extant tips, stop the experiment # and set the total lineage time for each lineage to be equal to # the minimum, to produce ultrametric trees. Also, the birth_scale # parameter of each leaf is rolled back to equal its parent's. if self.num_extant: - if len(current_lineages) == self.num_extant: + if current_lineages.qsize() == self.num_extant: remaining_lineages = [] - while len(current_lineages) > 0: - _, _, lineage = heapq.heappop(current_lineages) + while not current_lineages.empty(): + _, _, lineage = current_lineages.get() remaining_lineages.append(lineage) min_total_time = remaining_lineages[0]["total_time"] for lineage in remaining_lineages: @@ -322,7 +310,8 @@ def node_name_generator(start = 0) -> Generator[str, None, None]: observed_nodes.append(lineage["id"]) break # Pop the minimum age lineage to simulate forward time - _, _, lineage = heapq.heappop(current_lineages) + _, _, lineage = current_lineages.get() + # If the lineage is no longer active, just remove it from the queue. # This represents the time at which the lineage dies. if lineage["active"]: @@ -340,7 +329,8 @@ def node_name_generator(start = 0) -> Generator[str, None, None]: def sample_lineage_event( self, lineage: Dict[str, Union[int, float]], - current_lineages: List[Tuple[int, Any]], + current_lineages: PriorityQueue, + # current_lineages: List[Tuple[int, Any]], tree: nx.DiGraph, names: Generator, observed_nodes: List[str], @@ -410,8 +400,7 @@ def sample_lineage_event( tree.add_edge(lineage["id"], unique_id) tree.nodes[unique_id]["time"] = self.experiment_time - heapq.heappush( - current_lineages, + current_lineages.put( ( self.experiment_time, unique_id, @@ -421,7 +410,7 @@ def sample_lineage_event( "total_time": self.experiment_time, "active": False, }, - ), + ) ) # Indicate this node is observed at the end of experiment @@ -442,8 +431,7 @@ def sample_lineage_event( birth_waiting_time + lineage["total_time"] ) # Add the newly generated cell to the list of living lineages - heapq.heappush( - current_lineages, + current_lineages.put( ( birth_waiting_time + lineage["total_time"], unique_id, @@ -454,7 +442,7 @@ def sample_lineage_event( + lineage["total_time"], "active": True, }, - ), + ) ) @@ -465,8 +453,7 @@ def sample_lineage_event( tree.nodes[unique_id]["time"] = ( death_waiting_time + lineage["total_time"] ) - heapq.heappush( - current_lineages, + current_lineages.put( ( death_waiting_time + lineage["total_time"], unique_id, @@ -477,7 +464,7 @@ def sample_lineage_event( + lineage["total_time"], "active": False, }, - ), + ) ) def update_fitness(self, birth_scale: float) -> float: From 8bbcd1029b0c8f36aaf867df6df18b845c85d7d1 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Sat, 13 Apr 2024 16:57:20 -0700 Subject: [PATCH 26/37] moved ecDNA simulator back to PriorityQueue --- .../simulator/BirthDeathFitnessSimulator.py | 1 - .../simulator/ecDNABirthDeathSimulator.py | 29 +- notebooks/benchmark.ipynb | 2 +- notebooks/ecDNA_sim_test.ipynb | 426 ------------------ .../ecdna_birth_death_simulator_test.py | 35 +- 5 files changed, 29 insertions(+), 464 deletions(-) delete mode 100644 notebooks/ecDNA_sim_test.ipynb diff --git a/cassiopeia/simulator/BirthDeathFitnessSimulator.py b/cassiopeia/simulator/BirthDeathFitnessSimulator.py index bcd69d77..46711f49 100644 --- a/cassiopeia/simulator/BirthDeathFitnessSimulator.py +++ b/cassiopeia/simulator/BirthDeathFitnessSimulator.py @@ -330,7 +330,6 @@ def sample_lineage_event( self, lineage: Dict[str, Union[int, float]], current_lineages: PriorityQueue, - # current_lineages: List[Tuple[int, Any]], tree: nx.DiGraph, names: Generator, observed_nodes: List[str], diff --git a/cassiopeia/simulator/ecDNABirthDeathSimulator.py b/cassiopeia/simulator/ecDNABirthDeathSimulator.py index 4536294f..4c81c410 100644 --- a/cassiopeia/simulator/ecDNABirthDeathSimulator.py +++ b/cassiopeia/simulator/ecDNABirthDeathSimulator.py @@ -11,8 +11,7 @@ import numpy as np import pandas as pd -# from queue import PriorityQueue -import heapq +from queue import PriorityQueue from cassiopeia.data.CassiopeiaTree import CassiopeiaTree from cassiopeia.mixins import ecDNABirthDeathSimulatorError, TreeSimulatorError @@ -256,7 +255,7 @@ def update_fitness(self, ecdna_array: np.array) -> float: def sample_lineage_event( self, lineage: Dict[str, Union[int, float]], - current_lineages: List[Tuple[int, Any]], + current_lineages: PriorityQueue, tree: nx.DiGraph, names: Generator, observed_nodes: List[str], @@ -335,20 +334,17 @@ def sample_lineage_event( "ecdna_array" ] # child_ecdna_array - # Add the newly generated cell to the list of living lineages - heapq.heappush( - current_lineages, + current_lineages.put( ( birth_waiting_time + lineage["total_time"], unique_id, { "id": unique_id, "birth_scale": updated_birth_scale, - "total_time": birth_waiting_time - + lineage["total_time"], + "total_time": birth_waiting_time + lineage["total_time"], "active": True, }, - ), + ) ) return @@ -370,8 +366,7 @@ def sample_lineage_event( tree.nodes[unique_id]["ecdna_array"] = tree.nodes[lineage["id"]][ "ecdna_array" ] - heapq.heappush( - current_lineages, + current_lineages.put( ( self.experiment_time, unique_id, @@ -381,7 +376,7 @@ def sample_lineage_event( "total_time": self.experiment_time, "active": False, }, - ), + ) ) # Indicate this node is observed at the end of experiment @@ -405,8 +400,7 @@ def sample_lineage_event( ) tree.nodes[unique_id]["ecdna_array"] = child_ecdna_array # Add the newly generated cell to the list of living lineages - heapq.heappush( - current_lineages, + current_lineages.put( ( birth_waiting_time + lineage["total_time"], unique_id, @@ -417,7 +411,7 @@ def sample_lineage_event( + lineage["total_time"], "active": True, }, - ), + ) ) else: @@ -430,8 +424,7 @@ def sample_lineage_event( tree.nodes[unique_id]["ecdna_array"] = tree.nodes[ lineage["id"] ]["ecdna_array"] - heapq.heappush( - current_lineages, + current_lineages.put( ( death_waiting_time + lineage["total_time"], unique_id, @@ -442,7 +435,7 @@ def sample_lineage_event( + lineage["total_time"], "active": False, }, - ), + ) ) def get_ecdna_array(self, parent_id: str, tree: nx.DiGraph) -> np.array: diff --git a/notebooks/benchmark.ipynb b/notebooks/benchmark.ipynb index 9e20e3bf..d02fb285 100755 --- a/notebooks/benchmark.ipynb +++ b/notebooks/benchmark.ipynb @@ -607,7 +607,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.11.7" }, "vscode": { "interpreter": { diff --git a/notebooks/ecDNA_sim_test.ipynb b/notebooks/ecDNA_sim_test.ipynb deleted file mode 100644 index cd9b63b8..00000000 --- a/notebooks/ecDNA_sim_test.ipynb +++ /dev/null @@ -1,426 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cassiopeia ecDNA simulations\n", - "\n", - "Here, we simulate a population of cells with extrachromosomal DNA (ecDNA), circular, acentromeric DNA molecules that arise in cancers and segregate randomly at mitosis. This tutorial is based on benchmark.ipynb and we quote / borrow extensively from it. \n", - "\n", - "\"You can install Cassiopeia by following the guide [here](https://cassiopeia-lineage.readthedocs.io/en/latest/installation.html).\"\n", - "\n", - "\"All of our documentation is hosted [here](https://cassiopeia-lineage.readthedocs.io/en/latest/).\"" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "positional argument follows keyword argument (ecDNABirthDeathSimulator.py, line 401)", - "output_type": "error", - "traceback": [ - "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n", - " File \u001b[1;32m~/miniconda3/envs/ecass2/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3398\u001b[0m in \u001b[1;35mrun_code\u001b[0m\n exec(code_obj, self.user_global_ns, self.user_ns)\n", - " Input \u001b[1;32mIn [1]\u001b[0m in \u001b[1;35m\u001b[0m\n import cassiopeia as cas\n", - " File \u001b[1;32m~/miniconda3/envs/ecass2/lib/python3.9/site-packages/cassiopeia/__init__.py:10\u001b[0m in \u001b[1;35m\u001b[0m\n from . import simulator as sim\n", - "\u001b[0;36m File \u001b[0;32m~/miniconda3/envs/ecass2/lib/python3.9/site-packages/cassiopeia/simulator/__init__.py:8\u001b[0;36m in \u001b[0;35m\u001b[0;36m\u001b[0m\n\u001b[0;31m from .ecDNABirthDeathSimulator import ecDNABirthDeathSimulator\u001b[0m\n", - "\u001b[0;36m File \u001b[0;32m~/miniconda3/envs/ecass2/lib/python3.9/site-packages/cassiopeia/simulator/ecDNABirthDeathSimulator.py:401\u001b[0;36m\u001b[0m\n\u001b[0;31m cas_tree.set_attribute(node, attribute_name = \"ecdna_array\", tree.nodes[node][\"ecdna_array\"])\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n" - ] - } - ], - "source": [ - "import cProfile\n", - "from collections import defaultdict\n", - "import copy\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import time\n", - "from tqdm.auto import tqdm\n", - "\n", - "import cassiopeia as cas\n", - "from cassiopeia.solver import missing_data_methods" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import Image\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tree Simulation\n", - "\n", - "ecDNABirthDeathSimulator is a subclass of BirthDeathFitnesSimulator, which is documented [here](https://cassiopeia-lineage.readthedocs.io/en/latest/api/reference/cassiopeia.sim.BirthDeathFitnessSimulator.html). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bd_sim = cas.sim.ecDNABirthDeathSimulator(\n", - " birth_waiting_distribution = lambda scale: np.random.exponential(scale),\n", - " initial_birth_scale = 0.5,\n", - " death_waiting_distribution = lambda: np.inf, # lambda: np.random.exponential(1.5),\n", - " # mutation_distribution = lambda: 1 if np.random.uniform() < 0.5 else 0,\n", - " # fitness_distribution = lambda: np.random.normal(0, .5),\n", - " # fitness_base = 1.3,\n", - " num_extant = 4,\n", - " random_seed=17, \n", - " initial_copy_number = np.array([7,3,5]),\n", - " splitting_function = lambda x: np.random.binomial(x, p=0.5),\n", - " fitness_array = np.array([[[0,0.1], [0.1,0.2]],[[0.1,0.5],[0.2,0.6]]])\n", - ")\n", - "ground_truth_tree = bd_sim.simulate_tree()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can manually inspect the tree (when the number of cells is not too large) to see how random segregation leads to asymmetric CN. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# ground_truth_tree.__dict__ \n", - "ground_truth_tree._CassiopeiaTree__network.__dict__" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for node in ground_truth_tree.get_tree_topology().nodes:\n", - " print(node)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Unfortunately, iTOL is no longer free. However, there is local plotting functionality. For tutorials, see Cassiopeia/notebooks/local_plotting.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cas.pl.plot_matplotlib(ground_truth_tree, add_root=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's annotate the tree with the copy numbers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overlay Lineage Tracing Data\n", - "\n", - "We can then use our Cas9-based lineage tracing data simulator to overlay data. Here, this is a very flexible Cas9-data generator in which we can specify the dependence structure between characters (i.e. the cassettes that link togehter cut sites), the mutation rate, silencing rates, and state distributions. This function derives from the DataSimulator class & implements the `overlay_data` function which operates on a `CassiopeiaTree`, such as the one you simulated above. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(seed=None)\n", - "lt_sim = cas.sim.Cas9LineageTracingDataSimulator(\n", - " number_of_cassettes = 40,\n", - " size_of_cassette = 1,\n", - " mutation_rate = 0.1,\n", - " state_generating_distribution = lambda: np.random.exponential(1e-5),\n", - " number_of_states = 50,\n", - " state_priors = None,\n", - " heritable_silencing_rate = 9e-4,\n", - " stochastic_silencing_rate = 0.1,\n", - " heritable_missing_data_state = -1,\n", - " stochastic_missing_data_state = -1,\n", - ")\n", - "lt_sim.overlay_data(ground_truth_tree)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can get a feel for the character matrix and the complexity of the data here by extracting the simulated data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## inspect character matrix\n", - "\n", - "character_matrix = ground_truth_tree.character_matrix\n", - "\n", - "print(f\"We simulated {character_matrix.shape[1]} characters across {character_matrix.shape[0]} cells.\")\n", - "\n", - "# plot the number of unique states per character\n", - "character_matrix.nunique(axis=0).plot(kind='bar')\n", - "plt.xlabel(\"Character\")\n", - "plt.ylabel(\"Number of unique states \")\n", - "plt.show()\n", - "\n", - "missing_data_per_character = character_matrix.apply(lambda x: len(x[x == -1]) / len(x), axis=0)\n", - "missing_data_per_character.plot(kind='bar')\n", - "plt.xlabel(\"Character\")\n", - "plt.ylabel(\"Percentage of missing states\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Rebuild trees\n", - "\n", - "Cassiopeia has implemented several CassiopeiaSolvers for reconstructing trees. Each of these can take in several class-specific parameters and at a minimum implements the `solve` routine which operates on a CassiopeiaTree. \n", - "\n", - "The full list of solvers can be found [here](https://cassiopeia-lineage.readthedocs.io/en/latest/api/solver.html). For a full tutorial on tree reconstruction, refer to the [Tree Reconstruction notebook](https://github.com/YosefLab/Cassiopeia/blob/latest/notebooks/reconstruct.ipynb).\n", - "\n", - "Here we use the VanillaGreedySolver, which was described in the [Cassiopeia paper published in 2020](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02000-8)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "reconstructed_tree = cas.data.CassiopeiaTree(character_matrix = ground_truth_tree.character_matrix, missing_state_indicator = -1)\n", - "\n", - "greedy_solver = cas.solver.VanillaGreedySolver()\n", - "greedy_solver.solve(reconstructed_tree)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cas.pl.plot_matplotlib(reconstructed_tree, add_root=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Critique\n", - "\n", - "Our `critique` library is for testing the similarity between CassiopeiaTrees. The most pertinent use case is when comparing the reconstruction accuracy. We have two metrics implemented - a triplets correct measure, and the Robinson-Foulds metric." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "reconstructed_tree.collapse_mutationless_edges(infer_ancestral_characters = True)\n", - "\n", - "rf, rf_max = cas.critique.compare.robinson_foulds(ground_truth_tree, reconstructed_tree)\n", - "\n", - "print(f\"Normalized RF: {rf / rf_max}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "triplets = cas.critique.compare.triplets_correct(ground_truth_tree, reconstructed_tree, number_of_trials = 500)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building benchmarking pipelines\n", - "\n", - "We can link all of these steps together to benchmark the performance of several algorithms.\n", - "\n", - "The briefest of benchmarking pipelines would consist of:\n", - "\n", - "- Simulating a ground truth phylogeny\n", - "- Overlaying data onto this phylogeny\n", - "- Inferring the phylogeny with potentially many algorithms\n", - "- Assessing performance\n", - "\n", - "Here we show a very simple benchmarking pipeline:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "algorithms = {\"Vanilla Greedy\": cas.solver.VanillaGreedySolver(),\n", - " 'UPGMA': cas.solver.UPGMASolver(dissimilarity_function=cas.solver.dissimilarity.weighted_hamming_distance),\n", - " 'NeighborJoining': cas.solver.NeighborJoiningSolver(dissimilarity_function=cas.solver.dissimilarity.weighted_hamming_distance, add_root=True)\n", - " }\n", - "\n", - "iterations = 10\n", - "\n", - "algorithm_to_performance_triplets = defaultdict(list)\n", - "algorithm_to_performance_rf = defaultdict(list)\n", - "for _ in tqdm(range(iterations)):\n", - " \n", - " ground_truth_tree = bd_sim.simulate_tree()\n", - " \n", - " np.random.seed(None)\n", - " lt_sim.overlay_data(ground_truth_tree)\n", - " \n", - " for algorithm_name in tqdm(algorithms.keys()):\n", - " \n", - " algorithm = algorithms[algorithm_name]\n", - " \n", - " reconstructed_tree = cas.data.CassiopeiaTree(character_matrix = ground_truth_tree.character_matrix, missing_state_indicator = -1)\n", - " algorithm.solve(reconstructed_tree)\n", - " \n", - " # ground_truth_tree.collapse_mutationless_edges(infer_ancestral_characters = False)\n", - " reconstructed_tree.collapse_mutationless_edges(infer_ancestral_characters = True)\n", - "\n", - " rf, rf_max = cas.critique.compare.robinson_foulds(ground_truth_tree, reconstructed_tree)\n", - " \n", - " triplets = cas.critique.compare.triplets_correct(ground_truth_tree, reconstructed_tree, number_of_trials=500)\n", - " algorithm_to_performance_triplets[algorithm_name].append(np.mean(list(triplets[0].values())))\n", - " \n", - " algorithm_to_performance_rf[algorithm_name].append(rf / rf_max)\n", - "\n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can easily visualize results after reformatting the dictionaries of performances" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "algorithm_to_performance_triplets_df = pd.DataFrame(columns = ['Algorithm', 'Triplets'])\n", - "for algorithm in algorithm_to_performance_triplets:\n", - " \n", - " entries = algorithm_to_performance_triplets[algorithm]\n", - " new_df = pd.DataFrame([algorithm]*len(entries), columns = ['Algorithm'])\n", - " new_df['Triplets'] = entries\n", - " \n", - " algorithm_to_performance_triplets_df = pd.concat([algorithm_to_performance_triplets_df, new_df])\n", - " \n", - "algorithm_to_performance_rf_df = pd.DataFrame(columns = ['Algorithm', 'RF'])\n", - "for algorithm in algorithm_to_performance_rf:\n", - " \n", - " entries = algorithm_to_performance_rf[algorithm]\n", - " new_df = pd.DataFrame([algorithm]*len(entries), columns = ['Algorithm'])\n", - " new_df['RF'] = entries\n", - " \n", - " algorithm_to_performance_rf_df = pd.concat([algorithm_to_performance_rf_df, new_df])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sns.boxplot(data=algorithm_to_performance_triplets_df, x = 'Algorithm', y = 'Triplets')\n", - "plt.show()\n", - "\n", - "sns.boxplot(data=algorithm_to_performance_rf_df, x = 'Algorithm', y = 'RF')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(algorithm_to_performance_triplets_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - }, - "vscode": { - "interpreter": { - "hash": "7281e31e459af3472f32ee3e42bf55bc232e963b8420c818e7fe7d635207fd6b" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/test/simulator_tests/ecdna_birth_death_simulator_test.py b/test/simulator_tests/ecdna_birth_death_simulator_test.py index eba7d8fe..e73103e4 100644 --- a/test/simulator_tests/ecdna_birth_death_simulator_test.py +++ b/test/simulator_tests/ecdna_birth_death_simulator_test.py @@ -5,7 +5,7 @@ import heapq from typing import List, Tuple, Generator -# from queue import PriorityQueue +from queue import PriorityQueue import unittest import networkx as nx @@ -103,8 +103,7 @@ def test_initial_sample_event(self): tree.nodes[root]["birth_scale"] = 1 tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = np.array([3, 2, 5]) - # current_lineages = PriorityQueue() - current_lineages = [] + current_lineages = PriorityQueue() observed_nodes = [] starting_lineage = { "id": root, @@ -116,9 +115,9 @@ def test_initial_sample_event(self): sim.sample_lineage_event( starting_lineage, current_lineages, tree, names, observed_nodes ) - self.assertTrue(len(current_lineages) == 1) + self.assertTrue(current_lineages.qsize() == 1) - _, _, new_lineage = heapq.heappop(current_lineages) + _, _, new_lineage = current_lineages.get() self.assertTrue( np.all( tree.nodes[root]["ecdna_array"] @@ -148,7 +147,7 @@ def test_basic_sample_lineage_events(self): tree.nodes[root]["birth_scale"] = 1 tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = np.array([3, 2, 5]) - current_lineages = [] + current_lineages = PriorityQueue() observed_nodes = [] starting_lineage = { "id": root, @@ -161,7 +160,7 @@ def test_basic_sample_lineage_events(self): starting_lineage, current_lineages, tree, names, observed_nodes ) - _, _, new_lineage = heapq.heappop(current_lineages) + _, _, new_lineage = current_lineages.get() # now, let's do one normal division sim.sample_lineage_event( new_lineage, current_lineages, tree, names, observed_nodes @@ -172,10 +171,10 @@ def test_basic_sample_lineage_events(self): new_lineage, current_lineages, tree, names, observed_nodes ) - _, _, new_lineage = heapq.heappop(current_lineages) + _, _, new_lineage = current_lineages.get() total_time_d1 = new_lineage["total_time"] - _, _, new_lineage = heapq.heappop(current_lineages) + _, _, new_lineage = current_lineages.get() total_time_d2 = new_lineage["total_time"] # This need not be true in general, it's just b/c our birth_waiting_distribution is constant. @@ -194,7 +193,7 @@ def test_basic_sample_lineage_events(self): new_lineage, current_lineages, tree, names, observed_nodes ) - _, _, new_lineage = heapq.heappop(current_lineages) + _, _, new_lineage = current_lineages.get() self.assertTrue(observed_nodes[0] == new_lineage["id"]) self.assertTrue(new_lineage["total_time"] == 5) self.assertTrue(not (new_lineage["active"])) @@ -275,7 +274,7 @@ def test_basic_cosegregation(self): tree.nodes[root]["birth_scale"] = 1 tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = np.array([5, 5]) - current_lineages = [] + current_lineages = PriorityQueue() observed_nodes = [] starting_lineage = { "id": root, @@ -289,7 +288,7 @@ def test_basic_cosegregation(self): starting_lineage, current_lineages, tree, names, observed_nodes ) - _, _, new_lineage = heapq.heappop(current_lineages) + _, _, new_lineage = current_lineages.get() # now, let's do one normal division sim.sample_lineage_event( @@ -300,8 +299,8 @@ def test_basic_cosegregation(self): ) # get out the children - _, _, child_lineage_1 = heapq.heappop(current_lineages) - _, _, child_lineage_2 = heapq.heappop(current_lineages) + _, _, child_lineage_1 = current_lineages.get() + _, _, child_lineage_2 = current_lineages.get() # expected arrays derivation (with random seed 41): # ecdna1 segregates as (4, 6): @@ -348,7 +347,7 @@ def test_perfect_cosegregation(self): tree.nodes[root]["birth_scale"] = 1 tree.nodes[root]["time"] = 0 tree.nodes[root]["ecdna_array"] = np.array([5, 5]) - current_lineages = [] + current_lineages = PriorityQueue() observed_nodes = [] starting_lineage = { "id": root, @@ -362,7 +361,7 @@ def test_perfect_cosegregation(self): starting_lineage, current_lineages, tree, names, observed_nodes ) - _, _, new_lineage = heapq.heappop(current_lineages) + _, _, new_lineage = current_lineages.get() # now, let's do one normal division sim.sample_lineage_event( @@ -373,8 +372,8 @@ def test_perfect_cosegregation(self): new_lineage, current_lineages, tree, names, observed_nodes ) - _, _, child_lineage_1 = heapq.heappop(current_lineages) - _, _, child_lineage_2 = heapq.heappop(current_lineages) + _, _, child_lineage_1 = current_lineages.get() + _, _, child_lineage_2 = current_lineages.get() self.assertEqual( tree.nodes[child_lineage_1["id"]]["ecdna_array"][0], From 30b455251e89d1ad241224289be8ea259669afed Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Sat, 13 Apr 2024 17:09:59 -0700 Subject: [PATCH 27/37] rebuilding docs --- docs/api/simulator.rst | 1 + docs/conf.py | 4 +++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/docs/api/simulator.rst b/docs/api/simulator.rst index 40f51abe..7a3704cf 100644 --- a/docs/api/simulator.rst +++ b/docs/api/simulator.rst @@ -15,6 +15,7 @@ We have several frameworks available for simulating topologies: :toctree: reference/ sim.BirthDeathFitnessSimulator + sim.ecDNABirthDeathSimulator sim.CompleteBinarySimulator sim.SimpleFitSubcloneSimulator diff --git a/docs/conf.py b/docs/conf.py index 0032cd06..498daf5a 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -47,7 +47,7 @@ "sphinx.ext.autosummary", "scanpydoc.elegant_typehints", "scanpydoc.definition_list_typed_field", - "scanpydoc.autosummary_generate_imported", + # "scanpydoc.autosummary_generate_imported", *[p.stem for p in (HERE / "extensions").glob("*.py")], "sphinx_gallery.load_style", ] @@ -200,6 +200,8 @@ class AutoAutoSummary(Autosummary): "toctree": directives.unchanged, } + autosummary_imported_members = True + required_arguments = 1 @staticmethod From 189538e38a30618f9aad2692ffe5ee15b1e3a3c0 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Sat, 13 Apr 2024 17:18:49 -0700 Subject: [PATCH 28/37] added ecDNA simulator to user guide --- docs/user_guide.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/user_guide.rst b/docs/user_guide.rst index df3ac9c0..7dbcd667 100644 --- a/docs/user_guide.rst +++ b/docs/user_guide.rst @@ -14,6 +14,7 @@ Tutorials notebooks/benchmark notebooks/reconstruct notebooks/local_plotting + notebooks/simulate_ecDNA Contributed tutorials --------------------- From bffa6c00ec35b28145fe4a82825298728c071688 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Sat, 13 Apr 2024 17:31:30 -0700 Subject: [PATCH 29/37] added ecDNA simulator to user guide --- docs/conf.py | 1 + docs/user_guide.rst | 9 ++++++++- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 498daf5a..1e320207 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -165,6 +165,7 @@ "notebooks/benchmark": "_static/tutorials/benchmark.png", "notebooks/reconstruct": "_static/tutorials/reconstruct.png", "notebooks/local_plotting": "_static/tutorials/local_plotting.png", + "notebooks/simulate_ecDNA": "_static/tutorials/local_plotting.png" } diff --git a/docs/user_guide.rst b/docs/user_guide.rst index 7dbcd667..e3d2918a 100644 --- a/docs/user_guide.rst +++ b/docs/user_guide.rst @@ -5,7 +5,7 @@ Cassiopeia is a flexible tool for analyzing lineage-tracing data and benchmarkin For any questions about Cassiopeia, please file an issue on `Github `_. If you'd like to contribute a tutorial or a new algorithm, please follow our :doc:`Contributing guide`. -Tutorials +Main Tutorials ----------- .. nbgallery:: @@ -14,6 +14,13 @@ Tutorials notebooks/benchmark notebooks/reconstruct notebooks/local_plotting + + +Other Tutorials +----------------- + +.. toctree:: + notebooks/simulate_ecDNA Contributed tutorials From 4ef1270426580ddff7066ba7fff3c1877123e4e6 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Sat, 13 Apr 2024 17:38:08 -0700 Subject: [PATCH 30/37] added ecDNA notebook --- notebooks/simulate_ecDNA.ipynb | 383 +++++++++++++++++++++++++++++++++ 1 file changed, 383 insertions(+) create mode 100644 notebooks/simulate_ecDNA.ipynb diff --git a/notebooks/simulate_ecDNA.ipynb b/notebooks/simulate_ecDNA.ipynb new file mode 100644 index 00000000..2c5b47dc --- /dev/null +++ b/notebooks/simulate_ecDNA.ipynb @@ -0,0 +1,383 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cassiopeia ecDNA simulations\n", + "\n", + "Here, we simulate a population of cells with extrachromosomal DNA (ecDNA), circular, acentromeric DNA molecules that arise in cancers and segregate randomly at mitosis. The model implemented here is based on that described in [Hung*, Jones*, Wong*, Curtis*, et al. Coordinated inheritance of extrachromosomal DNA species in human cancer cells. BioRxiv 2023](https://www.biorxiv.org/content/10.1101/2023.07.18.549597v1).\n", + "\n", + "The simulation framework proceeds by simulating successive cell divisions whereby daughter cells recieve some fraction of the parent's ecDNA. It is assumed that ecDNA formation is a rare event and thus not included in this simulation framework (i.e., once a lineage loses ecDNA, it cannot regain it).\n", + "\n", + "In the simplest case, in each cell division an individual ecDNA copy passes to a daughter cell randomly; in other words, we expect the pool of ecDNA to be split up between daughter cells according to a Binomial random process. [Lange et al.](https://www.nature.com/articles/s41588-022-01177-x) previously described that this holds for individual ecDNA species. To model more sophisticated cases of ecDNA co-inheritance (as desribed in [Hung*, Jones*, Wong*, Curtis*, et al](https://www.biorxiv.org/content/10.1101/2023.07.18.549597v1)), users can make use of the `cosegregation` parameter. \n", + "\n", + "The simulation frameowrk extends the `BirthDeathFitnessSimlator`.\n", + "\n", + "You can install Cassiopeia by following the guide [here](https://cassiopeia-lineage.readthedocs.io/en/latest/installation.html).\n", + "\n", + "All of our documentation is hosted [here](https://cassiopeia-lineage.readthedocs.io/en/latest/)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import copy\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import scipy\n", + "import seaborn as sns\n", + "\n", + "import cassiopeia as cas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulating independent ecDNA species\n", + "\n", + "The basic unit in this simulation is referred to as an ecDNA \"species\", each of which can be thought of as a unique oncogene amplification. Here, we show how we can simulate multiple independent ecDNA species with this simulation framework and inspect results.\n", + "\n", + "While an arbitrary number of ecDNA species can be simulated, we simulate 2 independent species." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up parameters & simulate\n", + "\n", + "In addition to the basic parameters fed to `BirthDeathFitnessSimulator`, we specify parameters governing the ecDNA species. At a minimum, the user must provide a array of initial copy-numbers for the parental cell of length $d$ where $d$ is the number of ecDNA species you would like to simulate. In the example below, we simulate two species, both starting from an initial copy-number of 5.\n", + "\n", + "In addition, the user must provide fitness coefficients for each combination of ecDNAs carried in a cell. This is a $2^{d}$ matrix, where the entries denote the relative fitness of a cell carrying a particular combination of ecDNA. For example, in the $d=2$ case:\n", + "\n", + "$$S = \\begin{bmatrix} s_{0,0} & s_{0,1} \\\\ s_{1,0} & s_{1,1} \\end{bmatrix}$$\n", + "\n", + "Here $s_{0,0}$ denotes the fitness of cell carrying _neither_ ecDNA, $s_{0,1}$ denotes the fitness of a cell carrying only the second species (but not the first), and $s_{1,1}$ denotes the fitness of a cell carrying both ecDNA. The fitness matrix generalizes to 3 different species where, for example, $s_{0,0,1}$ would denote the fitness of a cell carrying the third species (and neither of the first two), and so forth." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "initial_birth_scale = 0.5\n", + "death_waiting_distribution = np.random.exponential(3)\n", + "num_extant = 200000\n", + "\n", + "# ecDNA parameters\n", + "initial_copy_number_array = np.array([5,5])\n", + "fitness_array = np.array([[0, 0.1],[0.1,0.1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removing 54285 sublineages.\n", + "CPU times: user 28.7 s, sys: 353 ms, total: 29.1 s\n", + "Wall time: 29.9 s\n" + ] + } + ], + "source": [ + "%%time\n", + "random_seed = 12345\n", + "\n", + "bd_sim = cas.sim.ecDNABirthDeathSimulator(\n", + " birth_waiting_distribution = lambda scale: np.random.exponential(1/scale),\n", + " initial_birth_scale = initial_birth_scale,\n", + " death_waiting_distribution = lambda: death_waiting_distribution,\n", + " num_extant = num_extant,\n", + " random_seed=random_seed,\n", + " initial_copy_number = initial_copy_number_array,\n", + " splitting_function = lambda c, x: c+np.random.binomial(x, p=0.5), # this means ecDNA is inherited randomly\n", + " fitness_array = fitness_array, \n", + ")\n", + "ground_truth_tree = bd_sim.simulate_tree()\n", + "\n", + "# subsample\n", + "subsampler = cas.sim.UniformLeafSubsampler(number_of_leaves = 1000)\n", + "ground_truth_tree = subsampler.subsample_leaves(ground_truth_tree)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Inspect results\n", + "\n", + "At a most basic level, we can inspect results by plotting the simulated phylogeny and looking at the joint distribution, especially the fraction of cells carrying at least one copy of both ecDNA species and the pearson correlation." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cas.pl.plot_matplotlib(ground_truth_tree, add_root=True, meta_data=['ecDNA_0', 'ecDNA_1'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ecDNA copy number correlation: -0.06738845071552513\n", + "38.8% of cells carried at least one copy of both ecDNA species\n", + "56.5% of cells carried at least one copy of ecDNA species 1.\n", + "71.0% of cells carried at least one copy of ecDNA species 2.\n" + ] + } + ], + "source": [ + "sns.scatterplot(x=ground_truth_tree.cell_meta['ecDNA_0'].values, y=ground_truth_tree.cell_meta['ecDNA_1'].values, alpha=0.1)\n", + "plt.xlabel(\"ecDNA_1 Copy Number\")\n", + "plt.ylabel(\"ecDNA_2 Copy Number\")\n", + "plt.title(\"Copy Number Joint distirbution\")\n", + "plt.show()\n", + "\n", + "counts = ground_truth_tree.cell_meta\n", + "cooccurrence = round(counts[\n", + " (counts[\"ecDNA_0\"] >= 1)\n", + " & (counts[\"ecDNA_1\"] >= 1)\n", + "].shape[0] / counts.shape[0] * 100, 2)\n", + "\n", + "ecdna_freq_1 = round(counts[\n", + " (counts[\"ecDNA_0\"] >= 1)\n", + "].shape[0] / counts.shape[0] * 100, 2)\n", + "\n", + "ecdna_freq_2 = round(counts[\n", + " (counts[\"ecDNA_1\"] >= 1)\n", + "].shape[0] / counts.shape[0] * 100, 2)\n", + "\n", + "correlation = scipy.stats.pearsonr(counts['ecDNA_0'].values, counts['ecDNA_1'].values)[0]\n", + "\n", + "print(f'ecDNA copy number correlation: {correlation}')\n", + "print(f'{cooccurrence}% of cells carried at least one copy of both ecDNA species')\n", + "\n", + "print(f'{ecdna_freq_1}% of cells carried at least one copy of ecDNA species 1.')\n", + "print(f'{ecdna_freq_2}% of cells carried at least one copy of ecDNA species 2.')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulate co-inheritance\n", + "\n", + "Our recent work has demonstrated that combinations of ecDNA can be co-inherited during mitosis, and our simulation framework can capture this phenomonen with the `cosegregation_coefficient` parameter. This parameter is a float between 0 and 1, where 0 denotes independent inheritance (as above) and 1 denotes perfect co-inheritance where all ecDNA the same fraction of each ecDNA is inherited into a daughter cell. Currently, this cosegregation parameter is only used with respect to the _first_ ecDNA species - in other words, more sophisitcated correlation matrices between multiple ecDNA cannot be modeled at this time." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up simulation and simulate" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "initial_birth_scale = 0.5\n", + "death_waiting_distribution = np.random.exponential(3)\n", + "num_extant = 200000\n", + "\n", + "# ecDNA parameters\n", + "initial_copy_number_array = np.array([5,5])\n", + "fitness_array = np.array([[0, 0.1],[0.1,0.3]])\n", + "cosegregation=0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removing 83405 sublineages.\n", + "CPU times: user 33.1 s, sys: 294 ms, total: 33.4 s\n", + "Wall time: 34.6 s\n" + ] + } + ], + "source": [ + "%%time\n", + "random_seed = 12345\n", + "\n", + "bd_sim = cas.sim.ecDNABirthDeathSimulator(\n", + " birth_waiting_distribution = lambda scale: np.random.exponential(1/scale),\n", + " initial_birth_scale = initial_birth_scale,\n", + " death_waiting_distribution = lambda: death_waiting_distribution,\n", + " num_extant = num_extant,\n", + " random_seed=random_seed,\n", + " initial_copy_number = initial_copy_number_array,\n", + " splitting_function = lambda c, x: c+np.random.binomial(x, p=0.5), # this means ecDNA is inherited randomly\n", + " fitness_array = fitness_array, \n", + " cosegregation_coefficient=cosegregation\n", + ")\n", + "ground_truth_tree = bd_sim.simulate_tree()\n", + "\n", + "# subsample\n", + "subsampler = cas.sim.UniformLeafSubsampler(number_of_leaves = 2000)\n", + "ground_truth_tree = subsampler.subsample_leaves(ground_truth_tree)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Inspect results\n", + "\n", + "Similarly, we can inspect results by viewing the phylogeny and the joint distribution. Notice that with the cosegregation paremeter included we see a marked improvement in the Pearson correlation, which we have described to be a hallmark of ecDNA co-inheritance." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cas.pl.plot_matplotlib(ground_truth_tree, add_root=True, meta_data=['ecDNA_0', 'ecDNA_1'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ecDNA copy number correlation: 0.4969485067875421\n", + "78.75% of cells carried at least one copy of both ecDNA species\n", + "84.7% of cells carried at least one copy of ecDNA species 1.\n", + "87.75% of cells carried at least one copy of ecDNA species 2.\n" + ] + } + ], + "source": [ + "sns.scatterplot(x=ground_truth_tree.cell_meta['ecDNA_0'].values, y=ground_truth_tree.cell_meta['ecDNA_1'].values, alpha=0.1)\n", + "plt.xlabel(\"ecDNA_1 Copy Number\")\n", + "plt.ylabel(\"ecDNA_2 Copy Number\")\n", + "plt.title(\"Copy Number Joint distirbution\")\n", + "plt.show()\n", + "\n", + "counts = ground_truth_tree.cell_meta\n", + "cooccurrence = round(counts[\n", + " (counts[\"ecDNA_0\"] >= 1)\n", + " & (counts[\"ecDNA_1\"] >= 1)\n", + "].shape[0] / counts.shape[0] * 100, 2)\n", + "\n", + "ecdna_freq_1 = round(counts[\n", + " (counts[\"ecDNA_0\"] >= 1)\n", + "].shape[0] / counts.shape[0] * 100, 2)\n", + "\n", + "ecdna_freq_2 = round(counts[\n", + " (counts[\"ecDNA_1\"] >= 1)\n", + "].shape[0] / counts.shape[0] * 100, 2)\n", + "\n", + "correlation = scipy.stats.pearsonr(counts['ecDNA_0'].values, counts['ecDNA_1'].values)[0]\n", + "\n", + "print(f'ecDNA copy number correlation: {correlation}')\n", + "print(f'{cooccurrence}% of cells carried at least one copy of both ecDNA species')\n", + "\n", + "print(f'{ecdna_freq_1}% of cells carried at least one copy of ecDNA species 1.')\n", + "print(f'{ecdna_freq_2}% of cells carried at least one copy of ecDNA species 2.')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ecnda-sim", + "language": "python", + "name": "ecnda-sim" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + }, + "vscode": { + "interpreter": { + "hash": "7281e31e459af3472f32ee3e42bf55bc232e963b8420c818e7fe7d635207fd6b" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From b6cb41c7135d0f48ee11f48a2f3925a03c13c558 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Sat, 13 Apr 2024 17:43:48 -0700 Subject: [PATCH 31/37] added ecDNA notebook --- docs/user_guide.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/user_guide.rst b/docs/user_guide.rst index e3d2918a..9e8e114a 100644 --- a/docs/user_guide.rst +++ b/docs/user_guide.rst @@ -20,7 +20,8 @@ Other Tutorials ----------------- .. toctree:: - + :maxdepth: 1 + notebooks/simulate_ecDNA Contributed tutorials From 10621c04c63c5320fb28ba5cdf3c22f1dd691eb8 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Mon, 15 Apr 2024 16:16:05 -0700 Subject: [PATCH 32/37] updated notebooks --- notebooks/reconstruct.ipynb | 9 +++++++-- notebooks/simulate_ecDNA.ipynb | 4 ++-- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/notebooks/reconstruct.ipynb b/notebooks/reconstruct.ipynb index cde32200..017fe2a5 100644 --- a/notebooks/reconstruct.ipynb +++ b/notebooks/reconstruct.ipynb @@ -1274,6 +1274,11 @@ "\n", "There are several parameters of interest which can all be explored on our [documentation website](https://cassiopeia-lineage.readthedocs.io/en/latest/api/reference/cassiopeia.solver.ILPSolver.html#cassiopeia.solver.ILPSolver). Because this process can take a long time, we'll restrict the potential graph layer size to 500 nodes and the convergence time to 500s. A more realistic solver might use our defaults - namely, a maximum potential graph layer size of 10,000 and a convergence time of 12,600s (3.5hr).\n", "\n", + "\n", + "
\n", + " Heads up: Please re-read the paragraph above - these settings are for the toy example, a more realistic solver will use the ILPSolver defaults.\n", + "
\n", + "\n", "The `ILPSolver` logs the progress of the potential graph inference and optimization in a user-defined logfile (by default, `stdout.log`). This logfile will also be output here.\n", "\n", "
\n", @@ -1566,7 +1571,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1580,7 +1585,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/notebooks/simulate_ecDNA.ipynb b/notebooks/simulate_ecDNA.ipynb index 2c5b47dc..8cc9dc4d 100644 --- a/notebooks/simulate_ecDNA.ipynb +++ b/notebooks/simulate_ecDNA.ipynb @@ -6,11 +6,11 @@ "source": [ "# Cassiopeia ecDNA simulations\n", "\n", - "Here, we simulate a population of cells with extrachromosomal DNA (ecDNA), circular, acentromeric DNA molecules that arise in cancers and segregate randomly at mitosis. The model implemented here is based on that described in [Hung*, Jones*, Wong*, Curtis*, et al. Coordinated inheritance of extrachromosomal DNA species in human cancer cells. BioRxiv 2023](https://www.biorxiv.org/content/10.1101/2023.07.18.549597v1).\n", + "Here, we simulate a population of cells with extrachromosomal DNA (ecDNA), circular, acentromeric DNA molecules that arise in cancers and segregate randomly at mitosis. The model implemented here is based on that described in [Hung\\*, Jones\\*, Wong\\*, Curtis\\*, et al. Coordinated inheritance of extrachromosomal DNA species in human cancer cells. BioRxiv 2023](https://www.biorxiv.org/content/10.1101/2023.07.18.549597v1).\n", "\n", "The simulation framework proceeds by simulating successive cell divisions whereby daughter cells recieve some fraction of the parent's ecDNA. It is assumed that ecDNA formation is a rare event and thus not included in this simulation framework (i.e., once a lineage loses ecDNA, it cannot regain it).\n", "\n", - "In the simplest case, in each cell division an individual ecDNA copy passes to a daughter cell randomly; in other words, we expect the pool of ecDNA to be split up between daughter cells according to a Binomial random process. [Lange et al.](https://www.nature.com/articles/s41588-022-01177-x) previously described that this holds for individual ecDNA species. To model more sophisticated cases of ecDNA co-inheritance (as desribed in [Hung*, Jones*, Wong*, Curtis*, et al](https://www.biorxiv.org/content/10.1101/2023.07.18.549597v1)), users can make use of the `cosegregation` parameter. \n", + "In the simplest case, in each cell division an individual ecDNA copy passes to a daughter cell randomly; in other words, we expect the pool of ecDNA to be split up between daughter cells according to a Binomial random process. [Lange et al.](https://www.nature.com/articles/s41588-022-01177-x) previously described that this holds for individual ecDNA species. To model more sophisticated cases of ecDNA co-inheritance (as desribed in [Hung\\*, Jones\\*, Wong\\*, Curtis\\*, et al](https://www.biorxiv.org/content/10.1101/2023.07.18.549597v1)), users can make use of the `cosegregation` parameter. \n", "\n", "The simulation frameowrk extends the `BirthDeathFitnessSimlator`.\n", "\n", From 77fc89b06a740b8fad11961e6309befd3cc2274f Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Mon, 15 Apr 2024 16:26:12 -0700 Subject: [PATCH 33/37] updated notebooks --- notebooks/reconstruct.ipynb | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/notebooks/reconstruct.ipynb b/notebooks/reconstruct.ipynb index 017fe2a5..65cce5e9 100644 --- a/notebooks/reconstruct.ipynb +++ b/notebooks/reconstruct.ipynb @@ -1292,7 +1292,10 @@ "metadata": {}, "outputs": [], "source": [ - "ilp_solver = cas.solver.ILPSolver(convergence_time_limit=500, maximum_potential_graph_layer_size=500, weighted=True, seed=1234)\n", + "ilp_solver = cas.solver.ILPSolver(convergence_time_limit=500, # recommended: 12600\n", + " maximum_potential_graph_layer_size=500, # recommended: 10000 \n", + " weighted=True,\n", + " seed=1234)\n", "ilp_solver.solve(cas_tree)" ] }, From 283cdc79e9961db27ac5a699377c5a2a6a3cb139 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Wed, 8 May 2024 10:56:54 -0700 Subject: [PATCH 34/37] updated formatting issues and docs in birthdeathfitnessimulator --- .../simulator/BirthDeathFitnessSimulator.py | 94 +++++++++++-------- 1 file changed, 57 insertions(+), 37 deletions(-) diff --git a/cassiopeia/simulator/BirthDeathFitnessSimulator.py b/cassiopeia/simulator/BirthDeathFitnessSimulator.py index 46711f49..31e07bda 100644 --- a/cassiopeia/simulator/BirthDeathFitnessSimulator.py +++ b/cassiopeia/simulator/BirthDeathFitnessSimulator.py @@ -3,6 +3,7 @@ process, including differing fitness on lineages on the tree. Allows for a variety of division and fitness regimes to be specified by the user. """ + from typing import Callable, Dict, Generator, List, Optional, Union import networkx as nx @@ -153,17 +154,28 @@ def __init__( self.initial_tree = initial_tree def initialize_tree(self, names) -> nx.DiGraph: - """initializes a tree (nx.DiGraph() object with one node). Auxiliary data for each node is grabbed from self (initial conditions / params) or hardcoded + """Initializes a tree. + + Initializes a tree (nx.DiGraph() object with one node). Auxiliary data + for each node is grabbed from self (initial conditions / params) or + hardcoded. - Args: names is a generator (function object that stores internal state) that will be used to generate names for the tree nodes - Output: tree (DiGraph object with one node, the root) - root (name of root node in tree) + Args: + names: A generator (function object that stores internal state) that + will be used to generate names for the tree nodes + Returns: + tree (DiGraph object with one node, the root) and root + (name of root node in tree) """ if self.initial_tree: tree = self.initial_tree.get_tree_topology() for node in self.initial_tree.nodes: - tree.nodes[node]['birth_scale'] = self.initial_tree.get_attribute(node, 'birth_scale') - tree.nodes[node]['time'] = self.initial_tree.get_attribute(node, 'time') + tree.nodes[node]["birth_scale"] = ( + self.initial_tree.get_attribute(node, "birth_scale") + ) + tree.nodes[node]["time"] = self.initial_tree.get_attribute( + node, "time" + ) return tree tree = nx.DiGraph() @@ -175,32 +187,34 @@ def initialize_tree(self, names) -> nx.DiGraph: return tree def make_initial_lineage_dict(self, tree: nx.DiGraph): - """ - uses self initial-conditions and hardcoded default parameters to create an initial lineage dict - Args: id_value: name of new lineage + """Makes initial lineage queue. + + Uses self initial-conditions and hardcoded default parameters to create + an initial lineage dict - Output: a lineage dict + Args: + id_value: name of new lineage + + Returns: + A lineage dict """ - + leaves = [node for node in tree if tree.out_degree(node) == 0] current_lineages = PriorityQueue() for leaf in leaves: - + lineage_dict = self.make_lineage_dict( - leaf, tree.nodes[leaf]['birth_scale'], tree.nodes[leaf]['time'], True - ) - + leaf, + tree.nodes[leaf]["birth_scale"], + tree.nodes[leaf]["time"], + True, + ) + if len(tree.nodes) == 1: return lineage_dict - - current_lineages.put( - ( - tree.nodes[leaf]['time'], - leaf, - lineage_dict - ) - ) - + + current_lineages.put((tree.nodes[leaf]["time"], leaf, lineage_dict)) + return current_lineages def make_lineage_dict( @@ -212,12 +226,14 @@ def make_lineage_dict( ): """makes a dict (lineage) from the given parameters. keys are hardcoded. - Args: id_value: id of new lineage - birth_scale: birth_scale parameter of new lineage - total_time: age of lineage - active_flag: bool to indicate whether lineage is active + Args: + id_value: id of new lineage + birth_scale: birth_scale parameter of new lineage + total_time: age of lineage + active_flag: bool to indicate whether lineage is active - Returns: a dict (lineage) with the parameter values under the hard-coded keys + Returns: + A dict (lineage) with the parameter values under the hard-coded keys """ lineage_dict = { @@ -250,7 +266,7 @@ def simulate_tree( TreeSimulatorError if all lineages die before a stopping condition """ - def node_name_generator(start = 0) -> Generator[str, None, None]: + def node_name_generator(start=0) -> Generator[str, None, None]: """Generates unique node names for the tree.""" i = start while True: @@ -259,23 +275,25 @@ def node_name_generator(start = 0) -> Generator[str, None, None]: starting_index = 0 if self.initial_tree: - starting_index = (np.max([int(l) for l in self.initial_tree.leaves]) + 1) + starting_index = ( + np.max([int(l) for l in self.initial_tree.leaves]) + 1 + ) names = node_name_generator(starting_index) # Set the seed if self.random_seed: np.random.seed(self.random_seed) - + tree = self.initialize_tree(names) - current_lineages = PriorityQueue() # instantiate queue + current_lineages = PriorityQueue() # instantiate queue # Records the nodes that are observed at the end of the experiment # TO DO: update to accept arbitrary fields in the dict. observed_nodes = [] starting_lineage = self.make_initial_lineage_dict(tree) - + if len(tree.nodes) == 1: # Sample the waiting time until the first division self.sample_lineage_event( @@ -444,7 +462,6 @@ def sample_lineage_event( ) ) - else: tree.add_node(unique_id) tree.nodes[unique_id]["birth_scale"] = lineage["birth_scale"] @@ -509,7 +526,8 @@ def populate_tree_from_simulation( """Populates tree with appropriate meta data. Args: - tree: The tree simulated with ecDNA and fitness values populated as attributes. + tree: The tree simulated with ecDNA and fitness values populated as + attributes. observed_nodes: The observed leaves of the tree. Returns: @@ -521,7 +539,9 @@ def populate_tree_from_simulation( time_dictionary = {} for i in tree.nodes: time_dictionary[i] = tree.nodes[i]["time"] - cassiopeia_tree.set_attribute(i, 'birth_scale', tree.nodes[i]['birth_scale']) + cassiopeia_tree.set_attribute( + i, "birth_scale", tree.nodes[i]["birth_scale"] + ) cassiopeia_tree.set_times(time_dictionary) # Prune dead lineages and collapse resulting unifurcations From a351abdf5867b68243b87db8c309e815dfd0a08b Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Wed, 8 May 2024 11:08:07 -0700 Subject: [PATCH 35/37] covered edge case where no birth scale is in initial tree --- .../simulator/BirthDeathFitnessSimulator.py | 11 ++-- .../birth_death_simulator_test.py | 50 +++++++++++++++++++ 2 files changed, 57 insertions(+), 4 deletions(-) diff --git a/cassiopeia/simulator/BirthDeathFitnessSimulator.py b/cassiopeia/simulator/BirthDeathFitnessSimulator.py index 31e07bda..b2d7e681 100644 --- a/cassiopeia/simulator/BirthDeathFitnessSimulator.py +++ b/cassiopeia/simulator/BirthDeathFitnessSimulator.py @@ -11,7 +11,7 @@ from queue import PriorityQueue from cassiopeia.data.CassiopeiaTree import CassiopeiaTree -from cassiopeia.mixins import TreeSimulatorError +from cassiopeia.mixins import CassiopeiaTreeError, TreeSimulatorError from cassiopeia.simulator.TreeSimulator import TreeSimulator @@ -170,9 +170,12 @@ def initialize_tree(self, names) -> nx.DiGraph: if self.initial_tree: tree = self.initial_tree.get_tree_topology() for node in self.initial_tree.nodes: - tree.nodes[node]["birth_scale"] = ( - self.initial_tree.get_attribute(node, "birth_scale") - ) + try: + tree.nodes[node]["birth_scale"] = ( + self.initial_tree.get_attribute(node, "birth_scale") + ) + except CassiopeiaTreeError: + tree.nodes[node]['birth_scale'] = self.initial_birth_scale tree.nodes[node]["time"] = self.initial_tree.get_attribute( node, "time" ) diff --git a/test/simulator_tests/birth_death_simulator_test.py b/test/simulator_tests/birth_death_simulator_test.py index 7bdd22f6..f4588270 100644 --- a/test/simulator_tests/birth_death_simulator_test.py +++ b/test/simulator_tests/birth_death_simulator_test.py @@ -417,6 +417,56 @@ def test_nonconstant_birth_death_with_variable_fitness(self): self.assertNotIn(2, tree.nodes) self.assertNotIn(3, tree.nodes) + def test_no_initial_birth_scale(self): + + topology = nx.DiGraph() + topology.add_edges_from( + [ + ("0", "1"), + ("0", "2"), + ("1", "3"), + ("1", "4"), + ("2", "5"), + ("2", "6"), + ] + ) + initial_tree = CassiopeiaTree(tree=topology) + + # initialize simulator with tree without default initial birth scales + birth_wd = lambda scale: np.random.exponential(scale) + + bd_sim = BirthDeathFitnessSimulator( + birth_wd, 1, num_extant=16, random_seed=54, initial_tree=initial_tree + ) + final_tree = bd_sim.simulate_tree() + + self.assertEqual(16, len(final_tree.leaves)) + for l in initial_tree.leaves: + birth_scale = final_tree.get_attribute(l, 'birth_scale') + self.assertEquals(1, birth_scale) + + def test_birth_scale(self): + + birth_wd = lambda scale: np.random.exponential(scale) + bd_sim_1 = BirthDeathFitnessSimulator( + birth_wd, 1, num_extant=16, random_seed=54 + ) + + initial_tree = bd_sim_1.simulate_tree() + + bd_sim_2 = BirthDeathFitnessSimulator( + birth_wd, 1, num_extant=100, random_seed=54, initial_tree=initial_tree + ) + final_tree = bd_sim_2.simulate_tree() + + self.assertEqual(100, len(final_tree.leaves)) + + for l in initial_tree.leaves: + birth_scale = initial_tree.get_attribute(l, 'birth_scale') + final_birth_scale = final_tree.get_attribute(l, 'birth_scale') + self.assertEquals(birth_scale, final_birth_scale) + + if __name__ == "__main__": unittest.main() From 83009eb3c82e538cc0adb2827681b012ed004063 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Wed, 8 May 2024 11:10:00 -0700 Subject: [PATCH 36/37] updated docs --- cassiopeia/simulator/BirthDeathFitnessSimulator.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/cassiopeia/simulator/BirthDeathFitnessSimulator.py b/cassiopeia/simulator/BirthDeathFitnessSimulator.py index b2d7e681..1467e064 100644 --- a/cassiopeia/simulator/BirthDeathFitnessSimulator.py +++ b/cassiopeia/simulator/BirthDeathFitnessSimulator.py @@ -95,7 +95,11 @@ class BirthDeathFitnessSimulator(TreeSimulator): collapse_unifurcations: Specifies whether to collapse unifurcations in the tree resulting from pruning dead lineages random_seed: A seed for reproducibility - initial_tree: A tree used for initializing the simulation. + initial_tree: A tree used for initializing the simulation. When this + argument is passed, a simulation will pick up from the leaves of the + specified tree. This can be useful for simulating trees when + selection may change over time (for example, in the presence + or absence of a drug pressure). Raises: TreeSimulatorError if invalid stopping conditions are provided or if a From fca94b2aeb22365117f81c0330c7c941155000c3 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Wed, 8 May 2024 11:11:48 -0700 Subject: [PATCH 37/37] removed old unused code --- cassiopeia/plotting/local.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/cassiopeia/plotting/local.py b/cassiopeia/plotting/local.py index 8bc13252..57b2df9f 100644 --- a/cassiopeia/plotting/local.py +++ b/cassiopeia/plotting/local.py @@ -185,7 +185,6 @@ def create_indel_heatmap( indel_colors: Optional[pd.DataFrame] = None, indel_priors: Optional[pd.DataFrame] = None, random_state: Optional[np.random.RandomState] = None, - clustered_linprof: Optional[pd.DataFrame] = None, ) -> Tuple[ List[ Dict[ @@ -408,6 +407,9 @@ def place_tree_and_annotations( colorstrips.extend(heatmap) # Any other annotations + if type(meta_data) == str: + meta_data = [meta_data] + for meta_item in meta_data: if meta_item not in tree.cell_meta.columns: raise PlottingError(