HOBBY -> WORK IN PROGRESS...

Oh well, some stuff is not implemented, some might not work as intended or might break, but overall a fun project. Enjoy :)

MSAexplorer

"Explore multiple sequence alignments with a simple python package."

DISCLAIMER: This is a hobby project of mine to keep my coding skills progressing. I re-implemented some of my old stuff and some new ideas, just to get it all under one roof. Hope it is useful for you. Contributions are highly welcome.

Requirements

Tried to make the requirements as minimal as possible.

• python >= 3.12
• matplotlib >= 3.9
• numpy ~ 2.1.3

Installation

git clone https://github.com/jonas-fuchs/MSAexplorer
cd MSAexplorer
pip install .

Documentation

The MSA class

from msaexplorer import explore

# load MSA
msa = explore.MSA('path-to-msa')

# you can set the zoom range and the reference id
msa.zoom = (0, 1500)  # set zoom range
msa.reference_id = 'your_ref_id'  # set reference id, this is used by different functions (otherwise consensus is used)

# access MSA attributes
msa.alignment  # alignment as dictionary
msa.length  # alignment length
msa.aln_type  # type of the alignment

# explore MSAs, outputs are largely dictionaries, some of the function are only for DNA/RNA
msa.get_snps()  # function to call snps compared to ref (if set) or consensus
msa.get_consensus()  # calculate a consensus sequence
msa.get_conserved_orfs()  # get orfs (including internal) with conserved start and stop position. identity is also calculated.
msa.get_reference_coords()  # get alignment coordinates of the reference
msa.get_non_overlapping_conserved_orfs()  # get orfs that do not overlap, additional a min identity can be set

msa.calc_gc()  # calc gc content
msa.calc_entropy()  # calc shannons normalized entropy
msa.calc_coverage()  # calc coverage
msa.calc_length_stats()  # calculate length statitstics
msa.calc_percent_recovery()  # calculated recovery over reference/consensus sequence
msa.calc_identity_alignment()  # convert the alignment into a identity alignment 
msa.calc_similarity_alignment()  # convert the alignment into a similartity alignment based on different similarity matrices
msa.calc_character_frequencies()  # calculate observed character frequencies
msa.calc_pairwise_identity_matrix()  # calculate identity matrix
msa.calc_reverse_complement_alignment()  # convert alignment sequences to reverese complement

Drawing an alignment with minimal python syntax

import matplotlib.pyplot as plt
from msaexplorer import explore
from msaexplorer import draw

# load alignment
aln = explore.MSA("example_alignments/BoDV.aln", reference_id=None, zoom_range=None)
# define subplots
fig, ax = plt.subplots(nrows=5, height_ratios=[0.5,0.5,0.5,2,1], sharex=False)
# draw different alignment vis
draw.stat_plot(aln, ax[0], "gc", rolling_average=50, line_color="black")
draw.stat_plot(aln, ax[1], stat_type="entropy", rolling_average=50, line_color="indigo")
draw.stat_plot(aln, ax[2], "coverage", rolling_average=1)
draw.identity_alignment(aln, ax[3], show_gaps=True, show_mask=True, show_mismatches=True, reference_color='lightsteelblue', show_seq_names=False, show_ambiguities=True, fancy_gaps=True, show_x_label=False, show_legend=True)
draw.similarity_alignment(aln, ax[4], show_x_label=True)
# format figure
fig.set_size_inches(14, 15)
fig.tight_layout()
plt.show()