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To learn more about WARP pipeline testing, see ",(0,r.kt)("a",{parentName:"p",href:"https://broadinstitute.github.io/warp/docs/About_WARP/TestingPipelines"},"Testing Pipelines"),"."),(0,r.kt)("h2",{id:"citing-the-slide-seq-pipeline"},"Citing the Slide-seq Pipeline"),(0,r.kt)("p",null,"If you use the Slide-seq Pipeline in your research, please identify the pipeline in your methods section using the ",(0,r.kt)("a",{parentName:"p",href:"https://scicrunch.org/resources/data/record/nlx_144509-1/SCR_023379/resolver?q=%22Slide-seq%22&l=%22Slide-seq%22&i=rrid:scr_023379"},"Slide-seq SciCrunch resource identifier"),"."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"Ex: ",(0,r.kt)("em",{parentName:"li"},"Slide-seq Pipeline (RRID:SCR_023379)"))),(0,r.kt)("p",null,"Please also consider citing our preprint:"),(0,r.kt)("p",null,"Degatano, K.; Awdeh, A.; Dingman, W.; Grant, G.; Khajouei, F.; Kiernan, E.; Konwar, K.; Mathews, K.; Palis, K.; Petrillo, N.; Van der Auwera, G.; Wang, C.; Way, J.; Pipelines, W. WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis. Preprints 2024, 2024012131. ",(0,r.kt)("a",{parentName:"p",href:"https://doi.org/10.20944/preprints202401.2131.v1"},"https://doi.org/10.20944/preprints202401.2131.v1")),(0,r.kt)("h2",{id:"consortia-support"},"Consortia support"),(0,r.kt)("p",null,"This pipeline is supported by the ",(0,r.kt)("a",{parentName:"p",href:"https://biccn.org/"},"BRAIN Initiative Cell Census Network")," (BICCN) and BRAIN Initiative Cell Atlas Network (BICAN). "),(0,r.kt)("p",null,"If your organization also uses this pipeline, we would like to list you! Please reach out to us by ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/issues"},"filing an issue in WARP"),"."),(0,r.kt)("h2",{id:"feedback"},"Feedback"),(0,r.kt)("p",null,"Please help us make our tools better by ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/issues"},"filing an issue in WARP"),"; we welcome pipeline-related suggestions or questions."))}m.isMDXComponent=!0},9716:function(e,t,a){t.Z=a.p+"assets/images/slide-seq_diagram-124fb3cf31b065e508227f5e006054ec.png"}}]); \ No newline at end of file diff --git a/assets/js/1944a93c.9044bb2b.js b/assets/js/1944a93c.9044bb2b.js new file mode 100644 index 0000000000..57779e9adc --- /dev/null +++ b/assets/js/1944a93c.9044bb2b.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkwebsite_2=self.webpackChunkwebsite_2||[]).push([[2595],{3905:function(e,t,i){i.d(t,{Zo:function(){return d},kt:function(){return m}});var n=i(7294);function a(e,t,i){return t in e?Object.defineProperty(e,t,{value:i,enumerable:!0,configurable:!0,writable:!0}):e[t]=i,e}function o(e,t){var i=Object.keys(e);if(Object.getOwnPropertySymbols){var n=Object.getOwnPropertySymbols(e);t&&(n=n.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),i.push.apply(i,n)}return i}function s(e){for(var t=1;t=0||(a[i]=e[i]);return a}(e,t);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(e);for(n=0;n=0||Object.prototype.propertyIsEnumerable.call(e,i)&&(a[i]=e[i])}return a}var l=n.createContext({}),u=function(e){var t=n.useContext(l),i=t;return e&&(i="function"==typeof e?e(t):s(s({},t),e)),i},d=function(e){var t=u(e.components);return n.createElement(l.Provider,{value:t},e.children)},c="mdxType",p={inlineCode:"code",wrapper:function(e){var t=e.children;return n.createElement(n.Fragment,{},t)}},f=n.forwardRef((function(e,t){var i=e.components,a=e.mdxType,o=e.originalType,l=e.parentName,d=r(e,["components","mdxType","originalType","parentName"]),c=u(i),f=a,m=c["".concat(l,".").concat(f)]||c[f]||p[f]||o;return i?n.createElement(m,s(s({ref:t},d),{},{components:i})):n.createElement(m,s({ref:t},d))}));function m(e,t){var i=arguments,a=t&&t.mdxType;if("string"==typeof e||a){var o=i.length,s=new Array(o);s[0]=f;var r={};for(var l in t)hasOwnProperty.call(t,l)&&(r[l]=t[l]);r.originalType=e,r[c]="string"==typeof e?e:a,s[1]=r;for(var u=2;u=0||(a[i]=e[i]);return a}(e,t);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(e);for(n=0;n=0||Object.prototype.propertyIsEnumerable.call(e,i)&&(a[i]=e[i])}return a}var l=n.createContext({}),u=function(e){var t=n.useContext(l),i=t;return e&&(i="function"==typeof e?e(t):s(s({},t),e)),i},d=function(e){var t=u(e.components);return n.createElement(l.Provider,{value:t},e.children)},c="mdxType",p={inlineCode:"code",wrapper:function(e){var t=e.children;return n.createElement(n.Fragment,{},t)}},f=n.forwardRef((function(e,t){var i=e.components,a=e.mdxType,o=e.originalType,l=e.parentName,d=r(e,["components","mdxType","originalType","parentName"]),c=u(i),f=a,m=c["".concat(l,".").concat(f)]||c[f]||p[f]||o;return i?n.createElement(m,s(s({ref:t},d),{},{components:i})):n.createElement(m,s({ref:t},d))}));function m(e,t){var i=arguments,a=t&&t.mdxType;if("string"==typeof e||a){var o=i.length,s=new Array(o);s[0]=f;var r={};for(var l in t)hasOwnProperty.call(t,l)&&(r[l]=t[l]);r.originalType=e,r[c]="string"==typeof e?e:a,s[1]=r;for(var u=2;u=0||(r[a]=t[a]);return r}(t,e);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(t);for(n=0;n=0||Object.prototype.propertyIsEnumerable.call(t,a)&&(r[a]=t[a])}return r}var o=n.createContext({}),p=function(t){var e=n.useContext(o),a=e;return t&&(a="function"==typeof t?t(e):l(l({},e),t)),a},d=function(t){var e=p(t.components);return n.createElement(o.Provider,{value:e},t.children)},u="mdxType",m={inlineCode:"code",wrapper:function(t){var e=t.children;return n.createElement(n.Fragment,{},e)}},c=n.forwardRef((function(t,e){var a=t.components,r=t.mdxType,i=t.originalType,o=t.parentName,d=s(t,["components","mdxType","originalType","parentName"]),u=p(a),c=r,h=u["".concat(o,".").concat(c)]||u[c]||m[c]||i;return a?n.createElement(h,l(l({ref:e},d),{},{components:a})):n.createElement(h,l({ref:e},d))}));function h(t,e){var a=arguments,r=e&&e.mdxType;if("string"==typeof t||r){var i=a.length,l=new Array(i);l[0]=c;var s={};for(var o in e)hasOwnProperty.call(e,o)&&(s[o]=e[o]);s.originalType=t,s[u]="string"==typeof t?t:r,l[1]=s;for(var p=2;pIllumina TruSeq Adapter Read 1\nAGATCGGAAGAGCACACGTCTGAACTCCAGTCA\n>Illumina TruSeq Adapter Read 2\nAGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT\n>polyA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n")),(0,r.kt)("h4",{id:"5-convert-fastqs-to-ubam"},"5. Convert FASTQs to uBAM"),(0,r.kt)("p",null,"The ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/tasks/broad/RNAWithUMIsTasks.wdl"},"tasks.FastqToUbam (alias = FastqToUbamAfterClipping)")," task converts trimmed FASTQs to an unmapped BAM using Picard's ",(0,r.kt)("a",{parentName:"p",href:"https://gatk.broadinstitute.org/hc/en-us/articles/360036510672"},"FastqToSam"),"."),(0,r.kt)("h4",{id:"6-alignment-with-star"},"6. Alignment with STAR"),(0,r.kt)("p",null,"After UMI extraction, the workflow aligns the paired-end reads to the reference (hg38 or hg19) using the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/alexdobin/STAR"},"STAR aligner"),', which is specifically designed for RNA-seq data and can align cDNA sequences with many "gaps" that correspond to introns. '),(0,r.kt)("p",null,"The task uses the following parameters:"),(0,r.kt)("table",null,(0,r.kt)("thead",{parentName:"table"},(0,r.kt)("tr",{parentName:"thead"},(0,r.kt)("th",{parentName:"tr",align:null},"Parameter"),(0,r.kt)("th",{parentName:"tr",align:null},"Value"),(0,r.kt)("th",{parentName:"tr",align:null},"Notes"))),(0,r.kt)("tbody",{parentName:"table"},(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"outSAMunmapped"),(0,r.kt)("td",{parentName:"tr",align:null},"Within"),(0,r.kt)("td",{parentName:"tr",align:null},"Includes unmapped reads in the output file rather than dropping those reads to facilitate potential downstream analysis.")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"outFilterMismatchNoverLmax"),(0,r.kt)("td",{parentName:"tr",align:null},"0.1"),(0,r.kt)("td",{parentName:"tr",align:null},"Sets the maximum allowable ratio of mismatches to read length. Reads with a ratio larger than the set value are filtered. For example, for paired-end reads with length 146, the reads are filtered if the number of mismatches is greater than 29 (146 ","*"," 2 ","*"," 0.1 = 29).")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"alignEndsProtrude"),(0,r.kt)("td",{parentName:"tr",align:null},"20 ConcordantPair"),(0,r.kt)("td",{parentName:"tr",align:null},"Allows a maximum of 20 protruding bases at alignment ends and marks these alignments as concordant pairs to prevent reads from small cDNA fragments that were sequenced into adapters from being dropped. This parameter allows for the processing of data derived from low-quality or degraded tissue such as formalin-fixed paraffin-embedded (FFPE) samples.")))),(0,r.kt)("p",null,"Additional parameters are used to match ",(0,r.kt)("a",{parentName:"p",href:"https://www.encodeproject.org/data-standards/rna-seq/long-rnas/"},"ENCODE bulk RNA-seq data standards"),". To learn more about ENCODE options in STAR, see the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"},"STAR manual"),"."),(0,r.kt)("p",null,"After STAR alignment, the workflow outputs both a genome- and transcriptome-aligned BAM."),(0,r.kt)("h4",{id:"7-mark-duplicates-and-sort-bams"},"7. Mark duplicates and sort BAMs"),(0,r.kt)("p",null,"As described in Step 2 (UMI extraction), UMIs are DNA tags that allow us to distinguish between PCR duplicates (duplicate reads with the same UMI) and biological duplicates (duplicate reads with different UMIs)."),(0,r.kt)("p",null,"In this step, the workflow sorts the aligned BAMs coordinates using Picard's ",(0,r.kt)("a",{parentName:"p",href:"https://gatk.broadinstitute.org/hc/en-us/articles/360036510732-SortSam-Picard-"},"SortSam")," tool and then groups the duplicates using UMI-tools' ",(0,r.kt)("a",{parentName:"p",href:"https://umi-tools.readthedocs.io/en/latest/reference/group.html#"},"group")," function. Once the duplicates are grouped by UMI, the PCR duplicates are marked using Picard's ",(0,r.kt)("a",{parentName:"p",href:"https://gatk.broadinstitute.org/hc/en-us/articles/360037052812-MarkDuplicates-Picard-"},"MarkDuplicates"),". This step outputs new genome- and transcriptome-aligned BAM files with PCR duplicates tagged and a corresponding index file."),(0,r.kt)("p",null,"The transcriptome-aligned BAM is then sorted using GATK\u2019s ",(0,r.kt)("a",{parentName:"p",href:"https://gatk.broadinstitute.org/hc/en-us/articles/5358861221659-PostProcessReadsForRSEM-BETA-"},"PostProcessReadsForRSEM")," for compatibility with ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/deweylab/RSEM"},"RSEM"),". While RSEM is not used in this workflow, it is an additional tool that can be used to quantify expression from RNA-seq data."),(0,r.kt)("h4",{id:"8-gene-quantification"},"8. Gene quantification"),(0,r.kt)("p",null,"After duplicate reads have been tagged, the workflow uses ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/getzlab/rnaseqc"},"RNA-SeQC")," to quantify the expression level of transcripts based on the number of reads that align to one or more exons of each gene (",(0,r.kt)("inlineCode",{parentName:"p"},"rnaseqc2_gene_counts"),"). Exon-level expression is also quantified based on the number of reads that align to each exon (",(0,r.kt)("inlineCode",{parentName:"p"},"rnaseqc2_exon_counts"),"). "),(0,r.kt)("h4",{id:"9-metric-calculation"},"9. Metric calculation"),(0,r.kt)("p",null,"The pipeline uses ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/s-andrews/FastQC"},"FastQC"),", ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/getzlab/rnaseqc"},"RNA-SeQC"),", Picard's ",(0,r.kt)("a",{parentName:"p",href:"https://gatk.broadinstitute.org/hc/en-us/articles/360037057492-CollectRnaSeqMetrics-Picard-"},"CollectRNASeqMetrics")," and ",(0,r.kt)("a",{parentName:"p",href:"https://gatk.broadinstitute.org/hc/en-us/articles/360037594031-CollectMultipleMetrics-Picard-"},"CollectMultipleMetrics")," tools, and GATK's ",(0,r.kt)("a",{parentName:"p",href:"https://gatk.broadinstitute.org/hc/en-us/articles/4418051471643-GetPileupSummaries"},"GetPileupSummaries")," and ",(0,r.kt)("a",{parentName:"p",href:"https://gatk.broadinstitute.org/hc/en-us/articles/4418054253211-CalculateContamination"},"CalculateContamination")," tools to calculate summary metrics that can be used to assess the quality of the data each time the pipeline is run. "),(0,r.kt)("p",null,"If you are a member of the Broad Institute's Genomics Platform using the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/broad/internal/rna_seq/BroadInternalRNAWithUMIs.wdl"},"internal RNA with UMIs pipeline"),", there is an additional step that merges the individual metrics files to create the ",(0,r.kt)("inlineCode",{parentName:"p"},"MergeMetrics.unified_metrics")," output file and prepare the data for use in the Terra Data Repository."),(0,r.kt)("h4",{id:"10-outputs"},"10. Outputs"),(0,r.kt)("p",null,"Workflow outputs are described in the table below. "),(0,r.kt)("table",null,(0,r.kt)("thead",{parentName:"table"},(0,r.kt)("tr",{parentName:"thead"},(0,r.kt)("th",{parentName:"tr",align:null},"Output variable name"),(0,r.kt)("th",{parentName:"tr",align:null},"Description"),(0,r.kt)("th",{parentName:"tr",align:null},"Type"))),(0,r.kt)("tbody",{parentName:"table"},(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"sample_name"),(0,r.kt)("td",{parentName:"tr",align:null},"Sample name extracted from the input unmapped BAM file header."),(0,r.kt)("td",{parentName:"tr",align:null},"String")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"transcriptome_bam"),(0,r.kt)("td",{parentName:"tr",align:null},"Duplicate-marked BAM file containing alignments from STAR translated into transcriptome coordinates and postprocessed for RSEM."),(0,r.kt)("td",{parentName:"tr",align:null},"BAM")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"transcriptome_duplicate_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},"File containing duplication metrics."),(0,r.kt)("td",{parentName:"tr",align:null},"TXT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"output_bam"),(0,r.kt)("td",{parentName:"tr",align:null},"Duplicate-marked BAM file containing alignments from STAR translated into genome coordinates."),(0,r.kt)("td",{parentName:"tr",align:null},"BAM")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"output_bam_index"),(0,r.kt)("td",{parentName:"tr",align:null},"Index file for the output_bam output."),(0,r.kt)("td",{parentName:"tr",align:null},"BAM Index")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"duplicate_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},"Duplicate metrics file containing the number of reads marked as duplicates."),(0,r.kt)("td",{parentName:"tr",align:null},"TXT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"rnaseqc2_gene_tpm"),(0,r.kt)("td",{parentName:"tr",align:null},"File containing TPMs."),(0,r.kt)("td",{parentName:"tr",align:null},"GCT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"rnaseqc2_gene_counts"),(0,r.kt)("td",{parentName:"tr",align:null},"File containing gene counts."),(0,r.kt)("td",{parentName:"tr",align:null},"GCT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"rnaseqc2_exon_counts"),(0,r.kt)("td",{parentName:"tr",align:null},"File containing exon counts."),(0,r.kt)("td",{parentName:"tr",align:null},"GCT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"rnaseqc2_fragment_size_histogram"),(0,r.kt)("td",{parentName:"tr",align:null},"File containing counts of observed fragment size."),(0,r.kt)("td",{parentName:"tr",align:null},"TXT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"rnaseqc2_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},"File containing RNA-SeQC metrics including strand specificity, 3\u2019/5\u2019 bias, rRNA reads, and others."),(0,r.kt)("td",{parentName:"tr",align:null},"TSV")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"picard_rna_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},"Metrics file containing the output of Picard\u2019s CollectRnaSeqMetrics tool."),(0,r.kt)("td",{parentName:"tr",align:null},"TXT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"picard_alignment_summary_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},"Metrics file containing output of Picard\u2019s CollectAlignmentSummaryMetrics tool."),(0,r.kt)("td",{parentName:"tr",align:null},"TXT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"picard_insert_size_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},"Metrics file containing output of Picard\u2019s CollectInsertSizeMetrics tool."),(0,r.kt)("td",{parentName:"tr",align:null},"TXT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"picard_insert_size_histogram"),(0,r.kt)("td",{parentName:"tr",align:null},"Histogram chart of insert size."),(0,r.kt)("td",{parentName:"tr",align:null},"PDF")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"picard_base_distribution_by_cycle_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},"Metrics file containing the output of Picard\u2019s CollectBaseDistributionByCycle tool."),(0,r.kt)("td",{parentName:"tr",align:null},"TXT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"picard_base_distribution_by_cycle_pdf"),(0,r.kt)("td",{parentName:"tr",align:null},"Chart of nucleotide distribution per cycle."),(0,r.kt)("td",{parentName:"tr",align:null},"PDF")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"picard_quality_by_cycle_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},"Metrics file containing the output of Picard\u2019s MeanQualityByCycle tool."),(0,r.kt)("td",{parentName:"tr",align:null},"TXT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"picard_quality_by_cycle_pdf"),(0,r.kt)("td",{parentName:"tr",align:null},"Chart of mean quality by 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WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis. Preprints 2024, 2024012131. ",(0,r.kt)("a",{parentName:"p",href:"https://doi.org/10.20944/preprints202401.2131.v1"},"https://doi.org/10.20944/preprints202401.2131.v1")),(0,r.kt)("h2",{id:"feedback"},"Feedback"),(0,r.kt)("p",null,"Please help us make our tools better by ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/issues"},"filing an issue in WARP"),"; we welcome pipeline-related suggestions or questions."))}m.isMDXComponent=!0},2434:function(t,e,a){e.Z=a.p+"assets/images/rna-with-umis_diagram-bce76bb42e6044bea0ff32d184622560.png"}}]); \ No newline at end of file diff --git a/assets/js/1d76e1a6.d5e248b8.js b/assets/js/1d76e1a6.d5e248b8.js new file mode 100644 index 0000000000..f807f44fda --- /dev/null +++ b/assets/js/1d76e1a6.d5e248b8.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkwebsite_2=self.webpackChunkwebsite_2||[]).push([[3188],{3905:function(t,e,a){a.d(e,{Zo:function(){return d},kt:function(){return h}});var n=a(7294);function r(t,e,a){return e in t?Object.defineProperty(t,e,{value:a,enumerable:!0,configurable:!0,writable:!0}):t[e]=a,t}function i(t,e){var a=Object.keys(t);if(Object.getOwnPropertySymbols){var n=Object.getOwnPropertySymbols(t);e&&(n=n.filter((function(e){return Object.getOwnPropertyDescriptor(t,e).enumerable}))),a.push.apply(a,n)}return a}function l(t){for(var e=1;e=0||(r[a]=t[a]);return r}(t,e);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(t);for(n=0;n=0||Object.prototype.propertyIsEnumerable.call(t,a)&&(r[a]=t[a])}return r}var o=n.createContext({}),p=function(t){var e=n.useContext(o),a=e;return t&&(a="function"==typeof t?t(e):l(l({},e),t)),a},d=function(t){var e=p(t.components);return n.createElement(o.Provider,{value:e},t.children)},u="mdxType",m={inlineCode:"code",wrapper:function(t){var e=t.children;return n.createElement(n.Fragment,{},e)}},c=n.forwardRef((function(t,e){var a=t.components,r=t.mdxType,i=t.originalType,o=t.parentName,d=s(t,["components","mdxType","originalType","parentName"]),u=p(a),c=r,h=u["".concat(o,".").concat(c)]||u[c]||m[c]||i;return a?n.createElement(h,l(l({ref:e},d),{},{components:a})):n.createElement(h,l({ref:e},d))}));function h(t,e){var a=arguments,r=e&&e.mdxType;if("string"==typeof t||r){var i=a.length,l=new Array(i);l[0]=c;var s={};for(var o in e)hasOwnProperty.call(e,o)&&(s[o]=e[o]);s.originalType=t,s[u]="string"==typeof t?t:r,l[1]=s;for(var p=2;pIllumina TruSeq Adapter Read 1\nAGATCGGAAGAGCACACGTCTGAACTCCAGTCA\n>Illumina TruSeq Adapter Read 2\nAGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT\n>polyA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n")),(0,r.kt)("h4",{id:"5-convert-fastqs-to-ubam"},"5. 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"),(0,r.kt)("table",null,(0,r.kt)("thead",{parentName:"table"},(0,r.kt)("tr",{parentName:"thead"},(0,r.kt)("th",{parentName:"tr",align:null},"Output variable name"),(0,r.kt)("th",{parentName:"tr",align:null},"Description"),(0,r.kt)("th",{parentName:"tr",align:null},"Type"))),(0,r.kt)("tbody",{parentName:"table"},(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"sample_name"),(0,r.kt)("td",{parentName:"tr",align:null},"Sample name extracted from the input unmapped BAM file header."),(0,r.kt)("td",{parentName:"tr",align:null},"String")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"transcriptome_bam"),(0,r.kt)("td",{parentName:"tr",align:null},"Duplicate-marked BAM file containing alignments from STAR translated into transcriptome coordinates and postprocessed for 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tool."),(0,r.kt)("td",{parentName:"tr",align:null},"TXT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"picard_base_distribution_by_cycle_pdf"),(0,r.kt)("td",{parentName:"tr",align:null},"Chart of nucleotide distribution per cycle."),(0,r.kt)("td",{parentName:"tr",align:null},"PDF")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"picard_quality_by_cycle_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},"Metrics file containing the output of Picard\u2019s MeanQualityByCycle tool."),(0,r.kt)("td",{parentName:"tr",align:null},"TXT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"picard_quality_by_cycle_pdf"),(0,r.kt)("td",{parentName:"tr",align:null},"Chart of mean quality by 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contamination."),(0,r.kt)("td",{parentName:"tr",align:null},"Float")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"contamination_error"),(0,r.kt)("td",{parentName:"tr",align:null},"Float representing the error associated with the contamination calculation."),(0,r.kt)("td",{parentName:"tr",align:null},"Float")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"fastqc_html_report"),(0,r.kt)("td",{parentName:"tr",align:null},"HTML report containing general quality control metrics generated by FastQC."),(0,r.kt)("td",{parentName:"tr",align:null},"File")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"fastqc_percent_reads_with_adapter"),(0,r.kt)("td",{parentName:"tr",align:null},"Float representing the percent of reads with adapter sequences present following the adapter clipping 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")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"When you update the changelog, the pipeline\u2019s WDL workflow version number must also match the changelog entry to ensure the updates pass the WARP testing process (this is shown in the ",(0,r.kt)("a",{parentName:"p",href:"/warp/docs/contribution/contribute_to_warp/contribution-tutorial"},"Example Contribution"),").")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"If you make any changes to files hosted in cloud repositories, like pipeline Docker images or reference files, coordinate with the WARP team during or before the review process to push the updated files to cloud storage. ")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"Remember to make necessary updates to the accompanying pipeline documentation such as the pipeline ",(0,r.kt)("a",{parentName:"p",href:"https://broadinstitute.github.io/warp/"},"overviews in WARP Documentation"),". 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This is why it's important to scope updates before proceeding."),(0,r.kt)("admonition",{title:"File an issue for large changes",type:"tip"},(0,r.kt)("p",{parentName:"admonition"},"If an update is large, or if you're unsure how a change affects multiple workflows, ",(0,r.kt)("strong",{parentName:"p"},(0,r.kt)("a",{parentName:"strong",href:"https://github.com/broadinstitute/warp/issues/new"},"file an issue")," in WARP first"),". ")),(0,r.kt)("p",null,"Filing an issue allows our team to provide valuable feedback before starting a large effort and appropriately prioritize the review work. "),(0,r.kt)("p",null,"Additionally, there are multiple requirements for our ",(0,r.kt)("a",{parentName:"p",href:"https://broadinstitute.github.io/warp/docs/About_WARP/TestingPipelines"},"testing")," infrastructure. By filing an issue for large updates, we can work with you right away to flag any potential testing-related issues. 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Pipeline changes must be documented in the changelog with the appropriate syntax."),(0,r.kt)("p",null,"Similarly, if you plan to contribute to WARP Documentation, read the ",(0,r.kt)("a",{parentName:"p",href:"https://broadinstitute.github.io/warp/docs/contribution/contribute_to_warp_docs/doc_style"},"WARP Documentation style guide")," first, as it contains helpful formatting tips."),(0,r.kt)("h4",{id:"3-make-a-branch-or-fork-off-the-warp-develop-branch"},"3. Make a branch or fork off the WARP develop branch."),(0,r.kt)("p",null,"If you have WARP repository permissions, GitHub will allow you to make a branch off the WARP ",(0,r.kt)("strong",{parentName:"p"},"develop")," branch when you\u2019re ready to contribute. For more information on how to make a branch, read the GitHub Docs ",(0,r.kt)("a",{parentName:"p",href:"https://docs.github.com/en/desktop/contributing-and-collaborating-using-github-desktop/making-changes-in-a-branch/managing-branches#creating-a-branch"},"instructions for branching"),". "),(0,r.kt)("p",null,"If you ",(0,r.kt)("strong",{parentName:"p"},"do not")," have WARP permissions (i.e. you can\u2019t make a branch off of the develop branch), make a fork of the WARP repository following the GitHub Docs ",(0,r.kt)("a",{parentName:"p",href:"https://docs.github.com/en/get-started/quickstart/fork-a-repo"},"instructions for forking"),"."),(0,r.kt)("p",null,(0,r.kt)("img",{src:n(7330).Z,width:"661",height:"360"})),(0,r.kt)("h4",{id:"4-make-your-updates-on-your-warp-branch-or-fork"},"4. Make your updates on your WARP branch or fork."),(0,r.kt)("p",null,"As your work progresses, make commits to your WARP branch or fork. 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")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"When you update the changelog, the pipeline\u2019s WDL workflow version number must also match the changelog entry to ensure the updates pass the WARP testing process (this is shown in the ",(0,r.kt)("a",{parentName:"p",href:"/warp/docs/contribution/contribute_to_warp/contribution-tutorial"},"Example Contribution"),").")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"If you make any changes to files hosted in cloud repositories, like pipeline Docker images or reference files, coordinate with the WARP team during or before the review process to push the updated files to cloud storage. ")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"Remember to make necessary updates to the accompanying pipeline documentation such as the pipeline ",(0,r.kt)("a",{parentName:"p",href:"https://broadinstitute.github.io/warp/"},"overviews in WARP Documentation"),". 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You\u2019ll receive a reminder to perform a ",(0,r.kt)("a",{parentName:"p",href:"https://docs.github.com/en/github/collaborating-with-pull-requests/incorporating-changes-from-a-pull-request/about-pull-request-merges#squash-and-merge-your-pull-request-commits"},"\u201csquash merge\u201d"),". Please delete individual commit comments and make one summary comment for all commits. "),(0,r.kt)("p",null,"If you don\u2019t have WARP permissions, the WARP team will merge the PR for you when it\u2019s ready."),(0,r.kt)("h2",{id:"review-process"},"Review Process"),(0,r.kt)("h3",{id:"warp-review-process-and-requirements"},"WARP review process and requirements"),(0,r.kt)("p",null,"After contributing a PR, a WARP team member will start a series of ",(0,r.kt)("a",{parentName:"p",href:"https://broadinstitute.github.io/warp/docs/About_WARP/TestingPipelines"},"tests"),", usually by making an \u201cok to test\u201d comment."),(0,r.kt)("p",null,"For each PR, WARP requires approval from a minimum of two developer reviewers. Additionally, depending on the changes, a review from a scientific owner or a clinical owner is also required, unless the developer is already the scientific or clinical owner. All comments and requests for changes are made directly in the GitHub PR. "),(0,r.kt)("p",null,"Comments will be likely be made within ",(0,r.kt)("strong",{parentName:"p"},"3 business days")," of the PR submission. If no review is started by that time, tag @Kylee Degatano in the PR. "),(0,r.kt)("p",null,"If a PR is abandoned after starting the review process, the WARP team will either take ownership of it or close the PR."),(0,r.kt)("h3",{id:"troubleshooting-warp-testing"},"Troubleshooting WARP testing"),(0,r.kt)("p",null,"All pipelines must pass syntax, scientific and Smart-tests, as described in the ",(0,r.kt)("a",{parentName:"p",href:"https://broadinstitute.github.io/warp/docs/About_WARP/TestingPipelines"},"testing overview"),". The WARP team will help troubleshoot testing for new contributions. 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Cost estimates per sample are provided below:"),(0,r.kt)("table",null,(0,r.kt)("thead",{parentName:"table"},(0,r.kt)("tr",{parentName:"thead"},(0,r.kt)("th",{parentName:"tr",align:null},"Cohort size ( # samples)"),(0,r.kt)("th",{parentName:"tr",align:null},"Cost per sample ($)"))),(0,r.kt)("tbody",{parentName:"table"},(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"1"),(0,r.kt)("td",{parentName:"tr",align:null},"8")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"10"),(0,r.kt)("td",{parentName:"tr",align:null},"0.8")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"100"),(0,r.kt)("td",{parentName:"tr",align:null},"0.11")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"1000"),(0,r.kt)("td",{parentName:"tr",align:null},"0.024")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"13.5 K"),(0,r.kt)("td",{parentName:"tr",align:null},"0.025")))),(0,r.kt)("h2",{id:"citing-the-imputation-pipeline"},"Citing the Imputation Pipeline"),(0,r.kt)("p",null,"If you use the Imputation Pipeline in your research, please consider citing our preprint:"),(0,r.kt)("p",null,"Degatano, K.; Awdeh, A.; Dingman, W.; Grant, G.; Khajouei, F.; Kiernan, E.; Konwar, K.; Mathews, K.; Palis, K.; Petrillo, N.; Van der Auwera, G.; Wang, C.; Way, J.; Pipelines, W. WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis. Preprints 2024, 2024012131. ",(0,r.kt)("a",{parentName:"p",href:"https://doi.org/10.20944/preprints202401.2131.v1"},"https://doi.org/10.20944/preprints202401.2131.v1")),(0,r.kt)("h2",{id:"contact-us"},"Contact us"),(0,r.kt)("p",null,"Help us make our tools better by ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/issues"},"filing an issue in WARP"),"; we welcome pipeline-related suggestions or questions."),(0,r.kt)("h2",{id:"licensing"},"Licensing"),(0,r.kt)("p",null,"Copyright Broad Institute, 2020 | BSD-3"),(0,r.kt)("p",null,"The workflow script is released under the ",(0,r.kt)("strong",{parentName:"p"},"WDL open source code license (BSD-3)")," (full license text at ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/LICENSE"},"https://github.com/broadinstitute/warp/blob/master/LICENSE"),"). However, please note that the programs it calls may be subject to different licenses. Users are responsible for checking that they are authorized to run all programs before running this script."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://github.com/broadinstitute/gatk/blob/master/LICENSE.TXT"},"GATK")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://github.com/broadinstitute/picard/blob/master/LICENSE.txt"},"Picard")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://alkesgroup.broadinstitute.org/Eagle/#x1-340007"},"Eagle2")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://github.com/statgen/Minimac4/blob/master/LICENSE"},"minimac4")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://github.com/samtools/bcftools/blob/develop/LICENSE"},"bcftools"),"\n-",(0,r.kt)("a",{parentName:"li",href:"http://vcftools.sourceforge.net/license.html"},"vcftools"))))}d.isMDXComponent=!0},5077:function(t,e,a){e.Z=a.p+"assets/images/imputation-fa25d2e8cbb65cb07b1da17932ca6be7.png"}}]); 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This matrix contains data from 12 library preparations total. "),(0,r.kt)("p",null,"Now let's look at the ",(0,r.kt)("inlineCode",{parentName:"p"},"cell_names")," column attribute which contains the unique cell barcodes:"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},">>> ds.ca.cell_names\narray(['GGACAAGAGTGCGTGA-0', 'GATCGATCACCAGGTC-0', 'AGCGGTCAGGGCTTGA-0',\n ..., 'GTACGTAAGCTATGCT-11', 'CAGAATCTCTGAGTGT-11',\n 'AACACGTAGTGTTTGC-11'], dtype=object)\n>>> \n")),(0,r.kt)("p",null,"The suffix appended to the barcodes in the ",(0,r.kt)("inlineCode",{parentName:"p"},"cell_names")," column is the index for the ",(0,r.kt)("inlineCode",{parentName:"p"},"input_id")," UUID to which the cell barcodes belong. "),(0,r.kt)("p",null,'For example, cell barcodes with a "-0" suffix belong to the library preparation represented by the first UUID, ',(0,r.kt)("inlineCode",{parentName:"p"},"166c1b1a-ad9c-4476-a4ec-8b52eb5032c7"),', whereas cell barcodes with a "-11" suffix represent the 10th UUID, ',(0,r.kt)("inlineCode",{parentName:"p"},"cbd23025-b1bf-4e9e-a297-ddab4a217b76"),"."),(0,r.kt)("h3",{id:"mapping-dcp-project-matrix-data-to-the-metadata-manifest"},"Mapping DCP project matrix data to the metadata manifest"),(0,r.kt)("p",null,"While the project matrices contain some project metadata (listed in the table above), there is additionally useful metadata in the project's metadata manifest, a TSV file containing all of a project's metadata, including donor and disease state information."),(0,r.kt)("p",null,"In addition to the global attribute ",(0,r.kt)("inlineCode",{parentName:"p"},"input_id"),", each project matrix has an ",(0,r.kt)("inlineCode",{parentName:"p"},"input_id")," column that can be useful for mapping matrix data back to the DCP metadata manifest. "),(0,r.kt)("p",null,"The values listed in the ",(0,r.kt)("inlineCode",{parentName:"p"},"input_id")," column match the library preparation UUID in the metadata manifest column ",(0,r.kt)("inlineCode",{parentName:"p"},"sequencing_process.provenance.document_id"),". "),(0,r.kt)("p",null,"Read more about the metadata manifest in the DCP ",(0,r.kt)("a",{parentName:"p",href:"https://data.humancellatlas.org/guides"},"Exploring Projects guide"),"."),(0,r.kt)("admonition",{title:"Explore HCA Project matrices in Terra",type:"tip"},(0,r.kt)("p",{parentName:"admonition"},"HCA matrices produced with Optimus are compatible with multiple downstream community analysis tools. For a tutorial on using the Optimus matrix with ",(0,r.kt)("a",{parentName:"p",href:"https://satijalab.org/seurat/index.html"},"Seurat"),", ",(0,r.kt)("a",{parentName:"p",href:"https://scanpy.readthedocs.io/en/stable/"},"Scanpy"),", ",(0,r.kt)("a",{parentName:"p",href:"https://cumulus.readthedocs.io/en/latest/index.html"},"Cumulus"),", or ",(0,r.kt)("a",{parentName:"p",href:"https://pegasus.readthedocs.io/en/stable/#"},"Pegasus"),", see the public ",(0,r.kt)("a",{parentName:"p",href:"https://app.terra.bio/#workspaces/featured-workspaces-hca/Intro-to-HCA-data-on-Terra"},"Intro-to-HCA-data-on-Terra workspace")," (login required) and its accompanying ",(0,r.kt)("a",{parentName:"p",href:"https://support.terra.bio/hc/en-us/articles/360060041772"},"step-by-step guide"),".")),(0,r.kt)("h3",{id:"mapping-dcp-project-matrix-data-to-the-contributor-matrices"},"Mapping DCP project matrix data to the contributor matrices"),(0,r.kt)("p",null,"Contributor matrices contain data analyzed and provided by the original project contributors. While they vary in format and content from project to project, they often include cell type annotations and additional metadata such as donor information and cell barcodes. "),(0,r.kt)("p",null,(0,r.kt)("em",{parentName:"p"},"If the contributor matrix contains donor metadata that matches a field in the project metadata manifest"),", the matrix can be linked to the DCP-generated project matrix in a two-step process."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"First, map the DCP matrix to the metadata manifest using the Loom's ",(0,r.kt)("inlineCode",{parentName:"p"},"input_id")," ",(0,r.kt)("strong",{parentName:"p"},"column"),"; this column contains the same library preparation/donor IDs as the project metadata manifest's ",(0,r.kt)("inlineCode",{parentName:"p"},"sequencing_process.provenance.document_id")," column. ")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"Second, map the contributor matrix to the metadata manifest using the contributor matrix column that matches the metadata manifest. "))),(0,r.kt)("p",null,"Contributor matrices might contain a column for cell barcodes for each library/preparation donor. These barcodes should match the non-unique barcodes listed in the DCP project matrix, with the exception of cells that might have been filtered out of the Loom matrix due to low UMIs. The Loom's non-unique barcodes are listed in the ",(0,r.kt)("inlineCode",{parentName:"p"},"CellID")," column. "),(0,r.kt)("p",null,(0,r.kt)("img",{src:a(5051).Z,width:"1720",height:"763"})),(0,r.kt)("p",null,"For example code showing how to link a contributor matrix to a DCP project matrix, see the ",(0,r.kt)("a",{target:"_blank",href:a(1612).Z},"Matrix_matching Jupyter Notebook"),"."),(0,r.kt)("p",null,"If you have any questions related to the contributor matrix and content, reach out to the individual project contributors listed on the Project page."),(0,r.kt)("h2",{id:"brain-initiative-cell-census-network-processing"},"Brain Initiative Cell Census Network Processing"),(0,r.kt)("p",null,"The Optimus pipeline supports data processing for the ",(0,r.kt)("a",{parentName:"p",href:"https://biccn.org/"},"BRAIN Initiative Cell Census Network (BICCN)"),". An overview of the BICCN pipeline resources is available on the BICCN's ",(0,r.kt)("a",{parentName:"p",href:"https://biccn.org/tools/biccn-pipelines"},"Pipelines page"),"."),(0,r.kt)("h3",{id:"optimus-reference-files-for-biccn-data-processing"},"Optimus reference files for BICCN data processing"),(0,r.kt)("p",null,"The BICCN 2.0 Whole Mouse Brain Working Group uses the Ensembl GRCm38 reference for alignment and a modified GTF for gene annotation (see table below). All Optimus pipeline reference inputs were created with the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/tree/master/pipelines/skylab/build_indices"},"BuildIndices workflow"),"."),(0,r.kt)("p",null,"BICCN processes single-nucleus data, which is enriched in pre-mRNAs containing introns. To account for this, the BuildIndices workflow uses the ",(0,r.kt)("inlineCode",{parentName:"p"},"BuildStarSingleNucleus")," task to add intron annotations to the GTF with a custom ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp-tools/blob/develop/3rd-party-tools/build-indices/add-introns-to-gtf.py"},"python script"),". The GTF contains all annotations for any ",(0,r.kt)("inlineCode",{parentName:"p"},"gene_id")," that has at least one transcript. This reduces the number of genes in the GTF to ","~","32,000. "),(0,r.kt)("p",null,"All reference files are available in a public Google bucket (see table below) and are accompanied by a README that details reference provenance (gs://gcp-public-data--broad-references/mm10/v0/README_mm10_singlecell_gencode.txt). "),(0,r.kt)("table",null,(0,r.kt)("thead",{parentName:"table"},(0,r.kt)("tr",{parentName:"thead"},(0,r.kt)("th",{parentName:"tr",align:null},"Optimus reference input name"),(0,r.kt)("th",{parentName:"tr",align:null},"Google bucket URI"),(0,r.kt)("th",{parentName:"tr",align:null},"Reference source"),(0,r.kt)("th",{parentName:"tr",align:null},"Description"))),(0,r.kt)("tbody",{parentName:"table"},(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("inlineCode",{parentName:"td"},"annotations_gtf")),(0,r.kt)("td",{parentName:"tr",align:null},"gs://gcp-public-data--broad-references/mm10/v0/single_nucleus/modified_gencode.vM23.primary_assembly.annotation.gtf"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("a",{parentName:"td",href:"https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M23/gencode.vM23.annotation.gtf.gzf"},"https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M23/gencode.vM23.annotation.gtf.gzf")),(0,r.kt)("td",{parentName:"tr",align:null},"Modified GENCODE GTF including intron annotations that can be used for intron counting with featureCounts.")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("inlineCode",{parentName:"td"},"ref_genome_fasta")),(0,r.kt)("td",{parentName:"tr",align:null},"gs://gcp-public-data--broad-references/mm10/v0/single_nucleus/modified_mm10.primary_assembly.genome.fa"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("a",{parentName:"td",href:"https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M23/GRCm38.p6.genome.fa.gz"},"https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M23/GRCm38.p6.genome.fa.gz")),(0,r.kt)("td",{parentName:"tr",align:null},"FASTA filed used to create the STAR reference files.")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("inlineCode",{parentName:"td"},"tar_star_reference")),(0,r.kt)("td",{parentName:"tr",align:null},"gs://gcp-public-data--broad-references/mm10/v0/single_nucleus/star/modified_star_2.7.9a_primary_gencode_mouse_vM23.tar"),(0,r.kt)("td",{parentName:"tr",align:null},"NA \u2014 built with the BuildIndices workflow."),(0,r.kt)("td",{parentName:"tr",align:null},"Reference files used for alignment with STAR.")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("inlineCode",{parentName:"td"},"whitelist")),(0,r.kt)("td",{parentName:"tr",align:null},"gs://broad-gotc-test-storage/Optimus/truth/plumbing/master/inputs/737K-august-2016.txt (for v2 chemistry) and gs://broad-gotc-test-storage/Optimus/truth/plumbing/master/inputs/3M-february-2018.txt (for v3 chemisty)"),(0,r.kt)("td",{parentName:"tr",align:null},"See ",(0,r.kt)("a",{parentName:"td",href:"https://kb.10xgenomics.com/hc/en-us/articles/115004506263-What-is-a-barcode-whitelist"},"10x barcode descriptions"),"."),(0,r.kt)("td",{parentName:"tr",align:null},"List of barcode sequences included in 10x library preparations. 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This matrix contains data from 12 library preparations total. "),(0,r.kt)("p",null,"Now let's look at the ",(0,r.kt)("inlineCode",{parentName:"p"},"cell_names")," column attribute which contains the unique cell barcodes:"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},">>> ds.ca.cell_names\narray(['GGACAAGAGTGCGTGA-0', 'GATCGATCACCAGGTC-0', 'AGCGGTCAGGGCTTGA-0',\n ..., 'GTACGTAAGCTATGCT-11', 'CAGAATCTCTGAGTGT-11',\n 'AACACGTAGTGTTTGC-11'], dtype=object)\n>>> \n")),(0,r.kt)("p",null,"The suffix appended to the barcodes in the ",(0,r.kt)("inlineCode",{parentName:"p"},"cell_names")," column is the index for the ",(0,r.kt)("inlineCode",{parentName:"p"},"input_id")," UUID to which the cell barcodes belong. "),(0,r.kt)("p",null,'For example, cell barcodes with a "-0" suffix belong to the library preparation represented by the first UUID, ',(0,r.kt)("inlineCode",{parentName:"p"},"166c1b1a-ad9c-4476-a4ec-8b52eb5032c7"),', whereas cell barcodes with a "-11" suffix represent the 10th UUID, ',(0,r.kt)("inlineCode",{parentName:"p"},"cbd23025-b1bf-4e9e-a297-ddab4a217b76"),"."),(0,r.kt)("h3",{id:"mapping-dcp-project-matrix-data-to-the-metadata-manifest"},"Mapping DCP project matrix data to the metadata manifest"),(0,r.kt)("p",null,"While the project matrices contain some project metadata (listed in the table above), there is additionally useful metadata in the project's metadata manifest, a TSV file containing all of a project's metadata, including donor and disease state information."),(0,r.kt)("p",null,"In addition to the global attribute ",(0,r.kt)("inlineCode",{parentName:"p"},"input_id"),", each project matrix has an ",(0,r.kt)("inlineCode",{parentName:"p"},"input_id")," column that can be useful for mapping matrix data back to the DCP metadata manifest. "),(0,r.kt)("p",null,"The values listed in the ",(0,r.kt)("inlineCode",{parentName:"p"},"input_id")," column match the library preparation UUID in the metadata manifest column ",(0,r.kt)("inlineCode",{parentName:"p"},"sequencing_process.provenance.document_id"),". "),(0,r.kt)("p",null,"Read more about the metadata manifest in the DCP ",(0,r.kt)("a",{parentName:"p",href:"https://data.humancellatlas.org/guides"},"Exploring Projects guide"),"."),(0,r.kt)("admonition",{title:"Explore HCA Project matrices in Terra",type:"tip"},(0,r.kt)("p",{parentName:"admonition"},"HCA matrices produced with Optimus are compatible with multiple downstream community analysis tools. For a tutorial on using the Optimus matrix with ",(0,r.kt)("a",{parentName:"p",href:"https://satijalab.org/seurat/index.html"},"Seurat"),", ",(0,r.kt)("a",{parentName:"p",href:"https://scanpy.readthedocs.io/en/stable/"},"Scanpy"),", ",(0,r.kt)("a",{parentName:"p",href:"https://cumulus.readthedocs.io/en/latest/index.html"},"Cumulus"),", or ",(0,r.kt)("a",{parentName:"p",href:"https://pegasus.readthedocs.io/en/stable/#"},"Pegasus"),", see the public ",(0,r.kt)("a",{parentName:"p",href:"https://app.terra.bio/#workspaces/featured-workspaces-hca/Intro-to-HCA-data-on-Terra"},"Intro-to-HCA-data-on-Terra workspace")," (login required) and its accompanying ",(0,r.kt)("a",{parentName:"p",href:"https://support.terra.bio/hc/en-us/articles/360060041772"},"step-by-step guide"),".")),(0,r.kt)("h3",{id:"mapping-dcp-project-matrix-data-to-the-contributor-matrices"},"Mapping DCP project matrix data to the contributor matrices"),(0,r.kt)("p",null,"Contributor matrices contain data analyzed and provided by the original project contributors. While they vary in format and content from project to project, they often include cell type annotations and additional metadata such as donor information and cell barcodes. "),(0,r.kt)("p",null,(0,r.kt)("em",{parentName:"p"},"If the contributor matrix contains donor metadata that matches a field in the project metadata manifest"),", the matrix can be linked to the DCP-generated project matrix in a two-step process."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"First, map the DCP matrix to the metadata manifest using the Loom's ",(0,r.kt)("inlineCode",{parentName:"p"},"input_id")," ",(0,r.kt)("strong",{parentName:"p"},"column"),"; this column contains the same library preparation/donor IDs as the project metadata manifest's ",(0,r.kt)("inlineCode",{parentName:"p"},"sequencing_process.provenance.document_id")," column. ")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"Second, map the contributor matrix to the metadata manifest using the contributor matrix column that matches the metadata manifest. "))),(0,r.kt)("p",null,"Contributor matrices might contain a column for cell barcodes for each library/preparation donor. These barcodes should match the non-unique barcodes listed in the DCP project matrix, with the exception of cells that might have been filtered out of the Loom matrix due to low UMIs. The Loom's non-unique barcodes are listed in the ",(0,r.kt)("inlineCode",{parentName:"p"},"CellID")," column. "),(0,r.kt)("p",null,(0,r.kt)("img",{src:a(5051).Z,width:"1720",height:"763"})),(0,r.kt)("p",null,"For example code showing how to link a contributor matrix to a DCP project matrix, see the ",(0,r.kt)("a",{target:"_blank",href:a(1612).Z},"Matrix_matching Jupyter Notebook"),"."),(0,r.kt)("p",null,"If you have any questions related to the contributor matrix and content, reach out to the individual project contributors listed on the Project page."),(0,r.kt)("h2",{id:"brain-initiative-cell-census-network-processing"},"Brain Initiative Cell Census Network Processing"),(0,r.kt)("p",null,"The Optimus pipeline supports data processing for the ",(0,r.kt)("a",{parentName:"p",href:"https://biccn.org/"},"BRAIN Initiative Cell Census Network (BICCN)"),". An overview of the BICCN pipeline resources is available on the BICCN's ",(0,r.kt)("a",{parentName:"p",href:"https://biccn.org/tools/biccn-pipelines"},"Pipelines page"),"."),(0,r.kt)("h3",{id:"optimus-reference-files-for-biccn-data-processing"},"Optimus reference files for BICCN data processing"),(0,r.kt)("p",null,"The BICCN 2.0 Whole Mouse Brain Working Group uses the Ensembl GRCm38 reference for alignment and a modified GTF for gene annotation (see table below). All Optimus pipeline reference inputs were created with the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/tree/master/pipelines/skylab/build_indices"},"BuildIndices workflow"),"."),(0,r.kt)("p",null,"BICCN processes single-nucleus data, which is enriched in pre-mRNAs containing introns. 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Insert tables"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-md"},"| Some Table Col 1 | Some Table Col 2 |\n| :--------------: | :--------------: |\n| Val1 | Val4 |\n| Val2 | Val5 |\n| Val3 | Val6 |\n")),(0,o.kt)("table",null,(0,o.kt)("thead",{parentName:"table"},(0,o.kt)("tr",{parentName:"thead"},(0,o.kt)("th",{parentName:"tr",align:"center"},"Some Table Col 1"),(0,o.kt)("th",{parentName:"tr",align:"center"},"Some Table Col 2"))),(0,o.kt)("tbody",{parentName:"table"},(0,o.kt)("tr",{parentName:"tbody"},(0,o.kt)("td",{parentName:"tr",align:"center"},"Val1"),(0,o.kt)("td",{parentName:"tr",align:"center"},"Val4")),(0,o.kt)("tr",{parentName:"tbody"},(0,o.kt)("td",{parentName:"tr",align:"center"},"Val2"),(0,o.kt)("td",{parentName:"tr",align:"center"},"Val5")),(0,o.kt)("tr",{parentName:"tbody"},(0,o.kt)("td",{parentName:"tr",align:"center"},"Val3"),(0,o.kt)("td",{parentName:"tr",align:"center"},"Val6")))),(0,o.kt)("admonition",{title:"TIP",type:"tip"},(0,o.kt)("p",{parentName:"admonition"},"It's worth mentioning that ",(0,o.kt)("a",{parentName:"p",href:"https://www.tablesgenerator.com/markdown_tables"},"Tables Generator")," is a great tool for generating and re-formatting markdown tables.")),(0,o.kt)("h2",{id:"3-cross-reference-and-anchor"},"3. Cross-reference and anchor"),(0,o.kt)("p",null,"To link to another section within the same article, you would use ",(0,o.kt)("inlineCode",{parentName:"p"},"[Return to ## 1. Insert code blocks](#1-insert-code-blocks)"),": ",(0,o.kt)("a",{parentName:"p",href:"#1-insert-code-blocks"},"Return to ## 1. 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\nCentered Text Block!\n
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\n\n![](./some_pic.png)\n\n
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Code groups"),(0,o.kt)("p",null,"You could also insert tab-based code groups:"),(0,o.kt)(N,{defaultValue:"apple",values:[{label:"Apple",value:"apple"},{label:"Orange",value:"orange"},{label:"Banana",value:"banana"}],mdxType:"Tabs"},(0,o.kt)(_,{value:"apple",mdxType:"TabItem"},(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-python"},'print("This is an apple pipeline!")\n'))),(0,o.kt)(_,{value:"orange",mdxType:"TabItem"},"This is an orange \ud83c\udf4a"),(0,o.kt)(_,{value:"banana",mdxType:"TabItem"},"This is a banana \ud83c\udf4c")),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-js"},"import Tabs from '@theme/Tabs';\nimport TabItem from '@theme/TabItem';\n\n\n This is an apple \ud83c\udf4e\n This is an orange \ud83c\udf4a\n This is a banana \ud83c\udf4c\n;\n")),(0,o.kt)("p",null,"For more details, please refer to ",(0,o.kt)("a",{parentName:"p",href:"https://docusaurus.io/docs/markdown-features/tabs"},"Docusaurus Docs")),(0,o.kt)("h2",{id:"8-footnotes"},"8. 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metrics by genes."),(0,r.kt)("td",{parentName:"tr",align:null},"Compressed CSV")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"aligner_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("inlineCode",{parentName:"td"},".star_metrics.tar")),(0,r.kt)("td",{parentName:"tr",align:null},"Tarred metrics files produced by the STARsolo aligner; contains align features, cell reads, summary, and UMI per cell metrics files."),(0,r.kt)("td",{parentName:"tr",align:null},"TXT")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"library_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("inlineCode",{parentName:"td"},"__library_metrics.csv")),(0,r.kt)("td",{parentName:"tr",align:null},"Optional CSV file containing all library-level metrics calculated with STARsolo for gene expression data. 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This matrix contains the unnormalized (unfiltered), UMI-corrected count matrices, as well as the gene and cell metrics detailed in the ",(0,r.kt)("a",{parentName:"p",href:"/warp/docs/Pipelines/Optimus_Pipeline/Loom_schema"},"Optimus Count Matrix Overview"),"."),(0,r.kt)("h4",{id:"try-the-optimus-matrix-with-community-tools"},"Try the Optimus matrix with community tools"),(0,r.kt)("p",null,"The matrix is compatible with multiple downstream community analysis tools, including ",(0,r.kt)("a",{parentName:"p",href:"https://satijalab.org/seurat/index.html"},"Seurat"),", ",(0,r.kt)("a",{parentName:"p",href:"https://scanpy.readthedocs.io/en/stable/"},"Scanpy"),", ",(0,r.kt)("a",{parentName:"p",href:"https://cumulus.readthedocs.io/en/latest/index.html"},"Cumulus"),", and ",(0,r.kt)("a",{parentName:"p",href:"https://pegasus.readthedocs.io/en/stable/#"},"Pegasus"),". To try a tutorial using the Optimus matrix with these tools, register for the open-source platform ",(0,r.kt)("a",{parentName:"p",href:"https://app.terra.bio"},"Terra")," and then navigate to the public ",(0,r.kt)("a",{parentName:"p",href:"https://app.terra.bio/#workspaces/featured-workspaces-hca/Intro-to-HCA-data-on-Terra"},"Intro-to-HCA-data-on-Terra workspace"),". You can also view the accompanying ",(0,r.kt)("a",{parentName:"p",href:"https://support.terra.bio/hc/en-us/articles/360060041772"},"step-by-step guide")," without registration."),(0,r.kt)("h2",{id:"validation-against-cell-ranger"},"Validation against Cell Ranger"),(0,r.kt)("p",null,"Optimus has been validated for processing both human and mouse single-cell and single-nucleus data (see links to validation reports in the table below). For each validation, Optimus results are compared to those of Cell Ranger (see the ",(0,r.kt)("a",{parentName:"p",href:"#faqs"},"FAQ")," for more on Cell Ranger comparisons)."),(0,r.kt)("table",null,(0,r.kt)("thead",{parentName:"table"},(0,r.kt)("tr",{parentName:"thead"},(0,r.kt)("th",{parentName:"tr",align:null},"Workflow configuration"),(0,r.kt)("th",{parentName:"tr",align:null},"Link to Report"))),(0,r.kt)("tbody",{parentName:"table"},(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"Human 10x v2 single-cell"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("a",{parentName:"td",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/benchmarking/v1_Apr2019/optimus_report.rst"},"Report"))),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"Mouse 10x v2 single-cell"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("a",{parentName:"td",href:"https://docs.google.com/document/d/1_3oO0ZQSrwEoe6D3GgKdSmAQ9qkzH_7wrE7x6_deL10/edit"},"Report"))),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"Human and mouse 10x v3 single-cell"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("a",{parentName:"td",href:"https://docs.google.com/document/d/1-hwfXkqtL8MblgDWFzk-HsVRYiy4PS8ZhJqAGlHBWYE/edit#heading=h.4uokn64v1s5m"},"Report"))),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"Human and Mouse 10x v2/v3 single-nucleus"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("a",{parentName:"td",href:"https://docs.google.com/document/d/1rv2M7vfpOzIOsMnMfNyKB4HV18lQ9dnOGHK2tPikiH0/edit"},"Report"))),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"Optimus STARsolo (v5.0.0 and later)"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("a",{parentName:"td",href:"https://docs.google.com/document/d/1B6Ux6HICD4ZL4Z0TG9LO-X43gOdF3sbq5qw_L2GA6fg/edit"},"Report"))))),(0,r.kt)("h2",{id:"versioning"},"Versioning"),(0,r.kt)("p",null,"All Optimus pipeline releases are documented in the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/Optimus.changelog.md"},"Optimus changelog"),"."),(0,r.kt)("h2",{id:"citing-the-optimus-pipeline"},"Citing the Optimus Pipeline"),(0,r.kt)("p",null,"If you use the Optimus Pipeline in your research, please identify the pipeline in your methods section using the ",(0,r.kt)("a",{parentName:"p",href:"https://scicrunch.org/resources/data/record/nlx_144509-1/SCR_018908/resolver?q=SCR_018908&l=SCR_018908&i=rrid:scr_018908"},"Optimus SciCrunch resource identifier"),"."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"Ex: ",(0,r.kt)("em",{parentName:"li"},"Optimus Pipeline (RRID:SCR_018908)"))),(0,r.kt)("p",null,"Please also consider citing our preprint:"),(0,r.kt)("p",null,"Degatano, K.; Awdeh, A.; Dingman, W.; Grant, G.; Khajouei, F.; Kiernan, E.; Konwar, K.; Mathews, K.; Palis, K.; Petrillo, N.; Van der Auwera, G.; Wang, C.; Way, J.; Pipelines, W. WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis. Preprints 2024, 2024012131. ",(0,r.kt)("a",{parentName:"p",href:"https://doi.org/10.20944/preprints202401.2131.v1"},"https://doi.org/10.20944/preprints202401.2131.v1")),(0,r.kt)("h2",{id:"consortia-support"},"Consortia support"),(0,r.kt)("p",null,"This pipeline is supported and used by the ",(0,r.kt)("a",{parentName:"p",href:"https://www.humancellatlas.org/"},"Human Cell Atlas")," (HCA) project and the ",(0,r.kt)("a",{parentName:"p",href:"https://biccn.org/"},"BRAIN Initiative Cell Census Network")," (BICCN). "),(0,r.kt)("p",null,"Each consortium may use slightly different reference files for data analysis or have different post-processing steps. Learn more by reading the ",(0,r.kt)("a",{parentName:"p",href:"/warp/docs/Pipelines/Optimus_Pipeline/consortia-processing"},"Consortia Processing")," overview."),(0,r.kt)("p",null,"If your organization also uses this pipeline, we would like to list you! Please reach out to us by ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/issues"},"filing an issue in WARP"),"."),(0,r.kt)("h2",{id:"feedback"},"Feedback"),(0,r.kt)("p",null,"Please help us make our tools better by ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/issues"},"filing an issue in WARP"),"; we welcome pipeline-related suggestions or questions."),(0,r.kt)("h2",{id:"acknowledgements"},"Acknowledgements"),(0,r.kt)("p",null,"We are immensely grateful to the members of the ",(0,r.kt)("a",{parentName:"p",href:"https://data.humancellatlas.org/"},"Human Cell Atlas Data Coordination Platform"),", BRAIN Initiative (",(0,r.kt)("a",{parentName:"p",href:"https://brainblog.nih.gov/brain-blog/brain-issues-suite-funding-opportunities-advance-brain-cell-atlases-through-centers"},"BICAN")," Sequencing Working Group) and ",(0,r.kt)("a",{parentName:"p",href:"https://nida.nih.gov/about-nida/organization/divisions/division-neuroscience-behavior-dnb/basic-research-hiv-substance-use-disorder/scorch-program"},"SCORCH")," for their invaluable and exceptional contributions to this pipeline. Our heartfelt appreciation goes to Alex Dobin, Aparna Bhaduri, Alec Wysoker, Anish Chakka, Brian Herb, Daofeng Li, Fenna Krienen, Guo-Long Zuo, Jeff Goldy, Kai Zhang, Khalid Shakir, Bo Li, Mariano Gabitto, Michael DeBerardine, Mengyi Song, Melissa Goldman, Nelson Johansen, James Nemesh, and Theresa Hodges for their unwavering dedication and remarkable efforts. "),(0,r.kt)("h2",{id:"faqs"},"FAQs"),(0,r.kt)("admonition",{title:"Question Can I run Optimus in Terra?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"Yes! We have a Terra workspace that is preconfigured with the latest Optimus workflow and is preloaded with human and mouse sample data. You can access the ",(0,r.kt)("a",{parentName:"p",href:"https://app.terra.bio/#workspaces/featured-workspaces-hca/HCA_Optimus_Pipeline"},"workspace"),". You will need a Google account to set up Terra. Please see ",(0,r.kt)("a",{parentName:"p",href:"https://support.terra.bio/hc/en-us"},"Terra Support")," for documents on getting started.")),(0,r.kt)("admonition",{title:"Question Is the output count matrix filtered or normalized?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"No, we do not filter. We keep as much data as possible so that the researcher can make their own filtering and normalization choices. We do, however, output some information that may be helpful for filtering, like UMI counts per cell and calls on whether or not a cell is empty from emptyDrops software. For the emptyDrops call, a cell will be flagged as possibly empty if it contains fewer than 100 molecules.")),(0,r.kt)("admonition",{title:"Question How does the workflow change when using the single-cell RNA-seq (counting_mode = 'sc_rna') vs. the single-nucleus (counting_mode = 'sn_rna') parameters?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"The counting_mode parameter is used to specify the STARsolo COUNTING_MODE; when sn_rna is specified, STARsolo will tag gene exons, UTRs, AND introns with the GX tag. Additionally, the Optimus uses the counting_mode to determine whether to run emptyDrops; no emptyDrops data is calculated for the sn_rna mode.")),(0,r.kt)("admonition",{title:"Question Where can I find example Optimus datasets and parameters to test the pipeline?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"There are four example configuration JSON files available for you to test the pipeline- the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/example_inputs/human_v2_example.json"},"human_v2_example.json"),", the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/example_inputs/human_v3_example.json"},"human_v3_example.json"),", the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/example_inputs/mouse_v2_snRNA_example.json"},"mouse_v2_snRNA_example.json"),", and the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/example_inputs/mouse_v2_snRNA_example.json"},"mouse_v2_snRNA_example.json"),"(see the Inputs section). Each of these configuration files can be run in the Optimus Featured Workspace in Terra at ",(0,r.kt)("a",{parentName:"p",href:"https://app.terra.bio/#workspaces/featured-workspaces-hca/HCA_Optimus_Pipeline"},"https://app.terra.bio/#workspaces/featured-workspaces-hca/HCA_Optimus_Pipeline"),", but you should note that the workspace comes preloaded with the same data and configurations.")),(0,r.kt)("p",null,"The Optimus pipeline is a single sample pipeline, but it can accept multiple FASTQ files if a sample is sequenced across lanes. In this case, the pipeline will merge the results from each lane into single output files. There will only be one merged file for each output type (i.e one h5ad matrix, etc.). If you would like to view an example configuration file for a multi-lane dataset, please see the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/example_inputs/mouse_v2_example.json"},"mouse_v2_example.json"),". Additionally, you can view sample outputs in the Optimus featured workspace on ",(0,r.kt)("a",{parentName:"p",href:"https://app.terra.bio/#workspaces/featured-workspaces-hca/HCA_Optimus_Pipeline"},"Terra"),".\n:::"),(0,r.kt)("admonition",{title:"Question How do I find which parameters and Docker images were used for the different tasks (i.e. STAR alignment, emptyDrops, etc.)",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"Parameters are listed in each task WDL. For a list of the tasks, see the table in the ",(0,r.kt)("a",{parentName:"p",href:"#optimus-tasks-and-tools"},"Tasks and Tools Section"),'. Select the link for the task of interest and then view the parameters in the task WDL "command {}" section. For the task Docker image, see task WDL "# runtime values" section; the Docker is listed as "String docker = ". If you want to learn more about all the different parameters available for a software tool, please select the relevant link in the table\'s "Tool" column.')),(0,r.kt)("admonition",{title:"Question Does Optimus have any read length requirements?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"For Read 1 sequences, the only minimum requirement is that reads are the combined lengths of the CB and UMIs (which will vary between 10x V1, V2, and V3 chemistry)."),(0,r.kt)("p",{parentName:"admonition"},"For Read 2 sequences, there is no read length requirement and read lengths will vary.")),(0,r.kt)("admonition",{title:"Question How does Optimus compare to Cell Ranger?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"Cell Ranger is a commonly used set of analysis pipelines developed by ",(0,r.kt)("a",{parentName:"p",href:"https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger"},"10x Genomics"),". Optimus and Cell Ranger share many features and additionally, Optimus results are validated against Cell Ranger results (see our ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/benchmarking/v1_Apr2019/optimus_report.rst"},"human validation report"),"). "),(0,r.kt)("p",{parentName:"admonition"},(0,r.kt)("em",{parentName:"p"},"So why develop an independent pipeline for 10x data analyses?")),(0,r.kt)("p",{parentName:"admonition"},"For three reasons:\n1) Need for an open-source, cloud-optimized pipeline. When Optimus was developed, Cell Ranger software was not yet open source, nor was it optimized for the cloud. To date, the Cell Ranger open-source code is still not regularly updated with Cell Ranger releases. In consequence, using the latest Cell Ranger (which is not open source yet) limits our ability to harness the breadth of tools available in the scientific community."),(0,r.kt)("p",{parentName:"admonition"},"2) Flexibility to process data similar, but not identical, to 10x. We wanted the ability to evolve our pipeline to process non-10x data types that might use similar features such as combinatorial indexing."),(0,r.kt)("p",{parentName:"admonition"},"3) Addition of metrics. We wanted the pipeline to calculate key metrics that would be useful to the scientific community, such as emptyDrops calculations, mitochondrial read metrics, etc."),(0,r.kt)("p",{parentName:"admonition"},(0,r.kt)("em",{parentName:"p"},"Reference differences between Optimus and Cell Ranger")),(0,r.kt)("p",{parentName:"admonition"},"Unlike Cell Ranger references, Optimus references are downloaded directly from GENCODE and not modified to remove pseudogenes and small RNAs. Learn more about Cell Ranger references on the ",(0,r.kt)("a",{parentName:"p",href:"https://support.10xgenomics.com/single-cell-multiome-atac-gex/software/release-notes/references#header"},"10x website"),". "),(0,r.kt)("p",{parentName:"admonition"},"In the case of multi-mapped pseudogenes, Optimus and Cell Ranger will produce different results. Optimus does not count multi-mapped reads in the final count matrix, whereas Cell Ranger will keep potential multi-mapped reads because it does not identify the pseudogene reads.")),(0,r.kt)("admonition",{title:"Question How does estimated cells differ between Cell Ranger and Optimus?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"Overall, the estimated cells produced by Optimus and Cell Ranger should only slightly vary. However, if you are using Optimus in the Multiome pipeline and trying to compare estimated cells to Cell Ranger ARC, you might find that ARC calls fewer cells. This is because ARC sets a threshold that both the ATAC and gene expression cells must pass, whereas Optimus is only setting a threshold for the gene expression side of the pipeline.")),(0,r.kt)("admonition",{type:"note"},(0,r.kt)("mdxAdmonitionTitle",{parentName:"admonition"},"Question What are ",(0,r.kt)("a",{parentName:"mdxAdmonitionTitle",href:"https://github.com/broadinstitute/warp/blob/develop/website/docs/Pipelines/Optimus_Pipeline/Library-metrics.md"},"library-level metrics")," in the Optimus pipeline?"),(0,r.kt)("p",{parentName:"admonition"},"Library-level metrics provide a summary of the sequencing library's quality and performance across all cells, as opposed to per-cell metrics. These metrics offer insights into the overall efficiency, coverage, and quality of the sequencing data produced.")),(0,r.kt)("admonition",{type:"note"},(0,r.kt)("mdxAdmonitionTitle",{parentName:"admonition"},"How are ",(0,r.kt)("a",{parentName:"mdxAdmonitionTitle",href:"https://github.com/broadinstitute/warp/blob/develop/website/docs/Pipelines/Optimus_Pipeline/Library-metrics.md"},"library-level metrics")," calculated in Optimus?"),(0,r.kt)("p",{parentName:"admonition"},"Library-level metrics in Optimus are calculated using a combination of STARsolo metrics and custom metrics as defined in the library metrics table linked in the actual documentation for gene expression data. These metrics assess key aspects like total reads, sequencing depth, and overall complexity of the library, offering a higher-level view of the data's quality.")))}u.isMDXComponent=!0},535:function(e,t,a){t.Z=a.p+"assets/images/Optimus_diagram-9ed86f08549489e7c475663f3e502f70.png"}}]); \ No newline at end of file diff --git a/assets/js/724ea057.17fbb1a2.js b/assets/js/724ea057.17fbb1a2.js new file mode 100644 index 0000000000..d97312f2f9 --- /dev/null +++ b/assets/js/724ea057.17fbb1a2.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkwebsite_2=self.webpackChunkwebsite_2||[]).push([[9799],{3905:function(e,t,a){a.d(t,{Zo:function(){return m},kt:function(){return h}});var n=a(7294);function r(e,t,a){return t in e?Object.defineProperty(e,t,{value:a,enumerable:!0,configurable:!0,writable:!0}):e[t]=a,e}function i(e,t){var a=Object.keys(e);if(Object.getOwnPropertySymbols){var n=Object.getOwnPropertySymbols(e);t&&(n=n.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),a.push.apply(a,n)}return a}function l(e){for(var t=1;t=0||(r[a]=e[a]);return r}(e,t);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);for(n=0;n=0||Object.prototype.propertyIsEnumerable.call(e,a)&&(r[a]=e[a])}return r}var o=n.createContext({}),p=function(e){var t=n.useContext(o),a=t;return e&&(a="function"==typeof e?e(t):l(l({},t),e)),a},m=function(e){var t=p(e.components);return n.createElement(o.Provider,{value:t},e.children)},d="mdxType",u={inlineCode:"code",wrapper:function(e){var t=e.children;return n.createElement(n.Fragment,{},t)}},c=n.forwardRef((function(e,t){var a=e.components,r=e.mdxType,i=e.originalType,o=e.parentName,m=s(e,["components","mdxType","originalType","parentName"]),d=p(a),c=r,h=d["".concat(o,".").concat(c)]||d[c]||u[c]||i;return a?n.createElement(h,l(l({ref:t},m),{},{components:a})):n.createElement(h,l({ref:t},m))}));function h(e,t){var a=arguments,r=t&&t.mdxType;if("string"==typeof e||r){var i=a.length,l=new Array(i);l[0]=c;var s={};for(var o in t)hasOwnProperty.call(t,o)&&(s[o]=t[o]);s.originalType=e,s[d]="string"==typeof e?e:r,l[1]=s;for(var p=2;p.bam")),(0,r.kt)("td",{parentName:"tr",align:null},"Aligned BAM"),(0,r.kt)("td",{parentName:"tr",align:null},"BAM")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"matrix"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("inlineCode",{parentName:"td"},"_sparse_counts.npz")),(0,r.kt)("td",{parentName:"tr",align:null},"Converted sparse matrix file from the MergeStarOutputs task."),(0,r.kt)("td",{parentName:"tr",align:null},"NPZ")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"matrix_row_index"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("inlineCode",{parentName:"td"},"_sparse_counts_row_index.npy")),(0,r.kt)("td",{parentName:"tr",align:null},"Index of cells in count matrix."),(0,r.kt)("td",{parentName:"tr",align:null},"NPY")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"matrix_col_index"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("inlineCode",{parentName:"td"},"_sparse_counts_col_index.npy")),(0,r.kt)("td",{parentName:"tr",align:null},"Index of genes in count matrix."),(0,r.kt)("td",{parentName:"tr",align:null},"NPY")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"cell_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("inlineCode",{parentName:"td"},".cell-metrics.csv.gz")),(0,r.kt)("td",{parentName:"tr",align:null},"Matrix of metrics by cells."),(0,r.kt)("td",{parentName:"tr",align:null},"Compressed CSV")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"gene_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("inlineCode",{parentName:"td"},".gene-metrics.csv.gz")),(0,r.kt)("td",{parentName:"tr",align:null},"Matrix of metrics by genes."),(0,r.kt)("td",{parentName:"tr",align:null},"Compressed CSV")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"aligner_metrics"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("inlineCode",{parentName:"td"},".star_metrics.tar")),(0,r.kt)("td",{parentName:"tr",align:null},"Tarred metrics files produced by the STARsolo aligner; contains align features, cell reads, summary, and UMI per cell metrics files. 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This matrix contains the unnormalized (unfiltered), UMI-corrected count matrices, as well as the gene and cell metrics detailed in the ",(0,r.kt)("a",{parentName:"p",href:"/warp/docs/Pipelines/Optimus_Pipeline/Loom_schema"},"Optimus Count Matrix Overview"),"."),(0,r.kt)("h4",{id:"try-the-optimus-matrix-with-community-tools"},"Try the Optimus matrix with community tools"),(0,r.kt)("p",null,"The matrix is compatible with multiple downstream community analysis tools, including ",(0,r.kt)("a",{parentName:"p",href:"https://satijalab.org/seurat/index.html"},"Seurat"),", ",(0,r.kt)("a",{parentName:"p",href:"https://scanpy.readthedocs.io/en/stable/"},"Scanpy"),", ",(0,r.kt)("a",{parentName:"p",href:"https://cumulus.readthedocs.io/en/latest/index.html"},"Cumulus"),", and ",(0,r.kt)("a",{parentName:"p",href:"https://pegasus.readthedocs.io/en/stable/#"},"Pegasus"),". To try a tutorial using the Optimus matrix with these tools, register for the open-source platform ",(0,r.kt)("a",{parentName:"p",href:"https://app.terra.bio"},"Terra")," and then navigate to the public ",(0,r.kt)("a",{parentName:"p",href:"https://app.terra.bio/#workspaces/featured-workspaces-hca/Intro-to-HCA-data-on-Terra"},"Intro-to-HCA-data-on-Terra workspace"),". You can also view the accompanying ",(0,r.kt)("a",{parentName:"p",href:"https://support.terra.bio/hc/en-us/articles/360060041772"},"step-by-step guide")," without registration."),(0,r.kt)("h2",{id:"validation-against-cell-ranger"},"Validation against Cell Ranger"),(0,r.kt)("p",null,"Optimus has been validated for processing both human and mouse single-cell and single-nucleus data (see links to validation reports in the table below). For each validation, Optimus results are compared to those of Cell Ranger (see the ",(0,r.kt)("a",{parentName:"p",href:"#faqs"},"FAQ")," for more on Cell Ranger comparisons)."),(0,r.kt)("table",null,(0,r.kt)("thead",{parentName:"table"},(0,r.kt)("tr",{parentName:"thead"},(0,r.kt)("th",{parentName:"tr",align:null},"Workflow configuration"),(0,r.kt)("th",{parentName:"tr",align:null},"Link to Report"))),(0,r.kt)("tbody",{parentName:"table"},(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"Human 10x v2 single-cell"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("a",{parentName:"td",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/benchmarking/v1_Apr2019/optimus_report.rst"},"Report"))),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"Mouse 10x v2 single-cell"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("a",{parentName:"td",href:"https://docs.google.com/document/d/1_3oO0ZQSrwEoe6D3GgKdSmAQ9qkzH_7wrE7x6_deL10/edit"},"Report"))),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"Human and mouse 10x v3 single-cell"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("a",{parentName:"td",href:"https://docs.google.com/document/d/1-hwfXkqtL8MblgDWFzk-HsVRYiy4PS8ZhJqAGlHBWYE/edit#heading=h.4uokn64v1s5m"},"Report"))),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"Human and Mouse 10x v2/v3 single-nucleus"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("a",{parentName:"td",href:"https://docs.google.com/document/d/1rv2M7vfpOzIOsMnMfNyKB4HV18lQ9dnOGHK2tPikiH0/edit"},"Report"))),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"Optimus STARsolo (v5.0.0 and later)"),(0,r.kt)("td",{parentName:"tr",align:null},(0,r.kt)("a",{parentName:"td",href:"https://docs.google.com/document/d/1B6Ux6HICD4ZL4Z0TG9LO-X43gOdF3sbq5qw_L2GA6fg/edit"},"Report"))))),(0,r.kt)("h2",{id:"versioning"},"Versioning"),(0,r.kt)("p",null,"All Optimus pipeline releases are documented in the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/Optimus.changelog.md"},"Optimus changelog"),"."),(0,r.kt)("h2",{id:"citing-the-optimus-pipeline"},"Citing the Optimus Pipeline"),(0,r.kt)("p",null,"If you use the Optimus Pipeline in your research, please identify the pipeline in your methods section using the ",(0,r.kt)("a",{parentName:"p",href:"https://scicrunch.org/resources/data/record/nlx_144509-1/SCR_018908/resolver?q=SCR_018908&l=SCR_018908&i=rrid:scr_018908"},"Optimus SciCrunch resource identifier"),"."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"Ex: ",(0,r.kt)("em",{parentName:"li"},"Optimus Pipeline (RRID:SCR_018908)"))),(0,r.kt)("p",null,"Please also consider citing our preprint:"),(0,r.kt)("p",null,"Degatano, K.; Awdeh, A.; Dingman, W.; Grant, G.; Khajouei, F.; Kiernan, E.; Konwar, K.; Mathews, K.; Palis, K.; Petrillo, N.; Van der Auwera, G.; Wang, C.; Way, J.; Pipelines, W. WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis. Preprints 2024, 2024012131. ",(0,r.kt)("a",{parentName:"p",href:"https://doi.org/10.20944/preprints202401.2131.v1"},"https://doi.org/10.20944/preprints202401.2131.v1")),(0,r.kt)("h2",{id:"consortia-support"},"Consortia support"),(0,r.kt)("p",null,"This pipeline is supported and used by the ",(0,r.kt)("a",{parentName:"p",href:"https://www.humancellatlas.org/"},"Human Cell Atlas")," (HCA) project and the ",(0,r.kt)("a",{parentName:"p",href:"https://biccn.org/"},"BRAIN Initiative Cell Census Network")," (BICCN). "),(0,r.kt)("p",null,"Each consortium may use slightly different reference files for data analysis or have different post-processing steps. Learn more by reading the ",(0,r.kt)("a",{parentName:"p",href:"/warp/docs/Pipelines/Optimus_Pipeline/consortia-processing"},"Consortia Processing")," overview."),(0,r.kt)("p",null,"If your organization also uses this pipeline, we would like to list you! Please reach out to us by ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/issues"},"filing an issue in WARP"),"."),(0,r.kt)("h2",{id:"feedback"},"Feedback"),(0,r.kt)("p",null,"Please help us make our tools better by ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/issues"},"filing an issue in WARP"),"; we welcome pipeline-related suggestions or questions."),(0,r.kt)("h2",{id:"acknowledgements"},"Acknowledgements"),(0,r.kt)("p",null,"We are immensely grateful to the members of the ",(0,r.kt)("a",{parentName:"p",href:"https://data.humancellatlas.org/"},"Human Cell Atlas Data Coordination Platform"),", BRAIN Initiative (",(0,r.kt)("a",{parentName:"p",href:"https://brainblog.nih.gov/brain-blog/brain-issues-suite-funding-opportunities-advance-brain-cell-atlases-through-centers"},"BICAN")," Sequencing Working Group) and ",(0,r.kt)("a",{parentName:"p",href:"https://nida.nih.gov/about-nida/organization/divisions/division-neuroscience-behavior-dnb/basic-research-hiv-substance-use-disorder/scorch-program"},"SCORCH")," for their invaluable and exceptional contributions to this pipeline. Our heartfelt appreciation goes to Alex Dobin, Aparna Bhaduri, Alec Wysoker, Anish Chakka, Brian Herb, Daofeng Li, Fenna Krienen, Guo-Long Zuo, Jeff Goldy, Kai Zhang, Khalid Shakir, Bo Li, Mariano Gabitto, Michael DeBerardine, Mengyi Song, Melissa Goldman, Nelson Johansen, James Nemesh, and Theresa Hodges for their unwavering dedication and remarkable efforts. "),(0,r.kt)("h2",{id:"faqs"},"FAQs"),(0,r.kt)("admonition",{title:"Question Can I run Optimus in Terra?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"Yes! We have a Terra workspace that is preconfigured with the latest Optimus workflow and is preloaded with human and mouse sample data. You can access the ",(0,r.kt)("a",{parentName:"p",href:"https://app.terra.bio/#workspaces/featured-workspaces-hca/HCA_Optimus_Pipeline"},"workspace"),". You will need a Google account to set up Terra. Please see ",(0,r.kt)("a",{parentName:"p",href:"https://support.terra.bio/hc/en-us"},"Terra Support")," for documents on getting started.")),(0,r.kt)("admonition",{title:"Question Is the output count matrix filtered or normalized?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"No, we do not filter. We keep as much data as possible so that the researcher can make their own filtering and normalization choices. We do, however, output some information that may be helpful for filtering, like UMI counts per cell and calls on whether or not a cell is empty from emptyDrops software. For the emptyDrops call, a cell will be flagged as possibly empty if it contains fewer than 100 molecules.")),(0,r.kt)("admonition",{title:"Question How does the workflow change when using the single-cell RNA-seq (counting_mode = 'sc_rna') vs. the single-nucleus (counting_mode = 'sn_rna') parameters?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"The counting_mode parameter is used to specify the STARsolo COUNTING_MODE; when sn_rna is specified, STARsolo will tag gene exons, UTRs, AND introns with the GX tag. Additionally, the Optimus uses the counting_mode to determine whether to run emptyDrops; no emptyDrops data is calculated for the sn_rna mode.")),(0,r.kt)("admonition",{title:"Question Where can I find example Optimus datasets and parameters to test the pipeline?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"There are four example configuration JSON files available for you to test the pipeline- the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/example_inputs/human_v2_example.json"},"human_v2_example.json"),", the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/example_inputs/human_v3_example.json"},"human_v3_example.json"),", the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/example_inputs/mouse_v2_snRNA_example.json"},"mouse_v2_snRNA_example.json"),", and the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/example_inputs/mouse_v2_snRNA_example.json"},"mouse_v2_snRNA_example.json"),"(see the Inputs section). Each of these configuration files can be run in the Optimus Featured Workspace in Terra at ",(0,r.kt)("a",{parentName:"p",href:"https://app.terra.bio/#workspaces/featured-workspaces-hca/HCA_Optimus_Pipeline"},"https://app.terra.bio/#workspaces/featured-workspaces-hca/HCA_Optimus_Pipeline"),", but you should note that the workspace comes preloaded with the same data and configurations.")),(0,r.kt)("p",null,"The Optimus pipeline is a single sample pipeline, but it can accept multiple FASTQ files if a sample is sequenced across lanes. In this case, the pipeline will merge the results from each lane into single output files. There will only be one merged file for each output type (i.e one h5ad matrix, etc.). If you would like to view an example configuration file for a multi-lane dataset, please see the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/example_inputs/mouse_v2_example.json"},"mouse_v2_example.json"),". Additionally, you can view sample outputs in the Optimus featured workspace on ",(0,r.kt)("a",{parentName:"p",href:"https://app.terra.bio/#workspaces/featured-workspaces-hca/HCA_Optimus_Pipeline"},"Terra"),".\n:::"),(0,r.kt)("admonition",{title:"Question How do I find which parameters and Docker images were used for the different tasks (i.e. STAR alignment, emptyDrops, etc.)",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"Parameters are listed in each task WDL. For a list of the tasks, see the table in the ",(0,r.kt)("a",{parentName:"p",href:"#optimus-tasks-and-tools"},"Tasks and Tools Section"),'. Select the link for the task of interest and then view the parameters in the task WDL "command {}" section. For the task Docker image, see task WDL "# runtime values" section; the Docker is listed as "String docker = ". If you want to learn more about all the different parameters available for a software tool, please select the relevant link in the table\'s "Tool" column.')),(0,r.kt)("admonition",{title:"Question Does Optimus have any read length requirements?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"For Read 1 sequences, the only minimum requirement is that reads are the combined lengths of the CB and UMIs (which will vary between 10x V1, V2, and V3 chemistry)."),(0,r.kt)("p",{parentName:"admonition"},"For Read 2 sequences, there is no read length requirement and read lengths will vary.")),(0,r.kt)("admonition",{title:"Question How does Optimus compare to Cell Ranger?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"Cell Ranger is a commonly used set of analysis pipelines developed by ",(0,r.kt)("a",{parentName:"p",href:"https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger"},"10x Genomics"),". Optimus and Cell Ranger share many features and additionally, Optimus results are validated against Cell Ranger results (see our ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/broadinstitute/warp/blob/master/pipelines/skylab/optimus/benchmarking/v1_Apr2019/optimus_report.rst"},"human validation report"),"). "),(0,r.kt)("p",{parentName:"admonition"},(0,r.kt)("em",{parentName:"p"},"So why develop an independent pipeline for 10x data analyses?")),(0,r.kt)("p",{parentName:"admonition"},"For three reasons:\n1) Need for an open-source, cloud-optimized pipeline. When Optimus was developed, Cell Ranger software was not yet open source, nor was it optimized for the cloud. To date, the Cell Ranger open-source code is still not regularly updated with Cell Ranger releases. In consequence, using the latest Cell Ranger (which is not open source yet) limits our ability to harness the breadth of tools available in the scientific community."),(0,r.kt)("p",{parentName:"admonition"},"2) Flexibility to process data similar, but not identical, to 10x. We wanted the ability to evolve our pipeline to process non-10x data types that might use similar features such as combinatorial indexing."),(0,r.kt)("p",{parentName:"admonition"},"3) Addition of metrics. We wanted the pipeline to calculate key metrics that would be useful to the scientific community, such as emptyDrops calculations, mitochondrial read metrics, etc."),(0,r.kt)("p",{parentName:"admonition"},(0,r.kt)("em",{parentName:"p"},"Reference differences between Optimus and Cell Ranger")),(0,r.kt)("p",{parentName:"admonition"},"Unlike Cell Ranger references, Optimus references are downloaded directly from GENCODE and not modified to remove pseudogenes and small RNAs. Learn more about Cell Ranger references on the ",(0,r.kt)("a",{parentName:"p",href:"https://support.10xgenomics.com/single-cell-multiome-atac-gex/software/release-notes/references#header"},"10x website"),". "),(0,r.kt)("p",{parentName:"admonition"},"In the case of multi-mapped pseudogenes, Optimus and Cell Ranger will produce different results. Optimus does not count multi-mapped reads in the final count matrix, whereas Cell Ranger will keep potential multi-mapped reads because it does not identify the pseudogene reads.")),(0,r.kt)("admonition",{title:"Question How does estimated cells differ between Cell Ranger and Optimus?",type:"note"},(0,r.kt)("p",{parentName:"admonition"},"Overall, the estimated cells produced by Optimus and Cell Ranger should only slightly vary. However, if you are using Optimus in the Multiome pipeline and trying to compare estimated cells to Cell Ranger ARC, you might find that ARC calls fewer cells. This is because ARC sets a threshold that both the ATAC and gene expression cells must pass, whereas Optimus is only setting a threshold for the gene expression side of the pipeline.")),(0,r.kt)("admonition",{type:"note"},(0,r.kt)("mdxAdmonitionTitle",{parentName:"admonition"},"Question What are ",(0,r.kt)("a",{parentName:"mdxAdmonitionTitle",href:"https://github.com/broadinstitute/warp/blob/develop/website/docs/Pipelines/Optimus_Pipeline/Library-metrics.md"},"library-level metrics")," in the Optimus pipeline?"),(0,r.kt)("p",{parentName:"admonition"},"Library-level metrics provide a summary of the sequencing library's quality and performance across all cells, as opposed to per-cell metrics. These metrics offer insights into the overall efficiency, coverage, and quality of the sequencing data produced.")),(0,r.kt)("admonition",{type:"note"},(0,r.kt)("mdxAdmonitionTitle",{parentName:"admonition"},"How are ",(0,r.kt)("a",{parentName:"mdxAdmonitionTitle",href:"https://github.com/broadinstitute/warp/blob/develop/website/docs/Pipelines/Optimus_Pipeline/Library-metrics.md"},"library-level metrics")," calculated in Optimus?"),(0,r.kt)("p",{parentName:"admonition"},"Library-level metrics in Optimus are calculated using a combination of STARsolo metrics and custom metrics as defined in the library metrics table linked in the actual documentation for gene expression data. 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WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis. Preprints 2024, 2024012131. ",(0,r.kt)("a",{parentName:"p",href:"https://doi.org/10.20944/preprints202401.2131.v1"},"https://doi.org/10.20944/preprints202401.2131.v1")),(0,r.kt)("h2",{id:"acknowledgements"},"Acknowledgements"),(0,r.kt)("p",null,"We are immensely grateful to the members of the BRAIN Initiative (BICAN Sequencing Working Group) and SCORCH for their invaluable and exceptional contributions to this pipeline. 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