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Graph Name Retrieved From View
workflow graph gathered exome alignment and somatic variant detection for cle purpose

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/somatic_exome_cle_gathered.cwl

Branch/Commit ID: 9cbf2a483e1b9e4cdb8e2564be27a9e64fc1169e
workflow graph Generate genome indices for STAR & bowtie

Creates indices for: * [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) * [bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) It performs the following steps: 1. `STAR --runMode genomeGenerate` to generate indices, based on [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) and [GTF](http://mblab.wustl.edu/GTF2.html) input files, returns results as an array of files 2. Outputs indices as [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates *chrNameLength.txt* file from Directory output 4. `bowtie-build` to generate indices requires genome [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) file as input, returns results as a group of main and secondary files

https://github.com/datirium/workflows.git

Path: workflows/genome-indices.cwl

Branch/Commit ID: 42dc4f70b117e78785b82865ec4c4b941ac1c259
workflow graph Align reference proteins plane complete workflow

https://github.com/ncbi/pgap.git

Path: protein_alignment/wf_protein_alignment.cwl

Branch/Commit ID: 551493f5c24b757a46cd22821a05e6ac6dcceb7f
workflow graph Subworkflow to allow calling different SV callers which require bam files as inputs

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/single_sample_sv_callers.cwl

Branch/Commit ID: 0d2f354af9192a56af258a7d2426c7c160f4ec1a
workflow graph advanced-header.cwl

https://github.com/datirium/workflows.git

Path: metadata/advanced-header.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869
workflow graph ROSE: rank ordering of super-enhancers

Super-enhancers, consist of clusters of enhancers that are densely occupied by the master regulators and Mediator. Super-enhancers differ from typical enhancers in size, transcription factor density and content, ability to activate transcription, and sensitivity to perturbation. Use to create stitched enhancers, and to separate super-enhancers from typical enhancers using sequencing data (.bam) given a file of previously identified constituent enhancers (.gff)

 https://github.com/datirium/workflows.git

Path: workflows/super-enhancer.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869
workflow graph Single-Cell RNA-Seq Filtering Analysis

Single-Cell RNA-Seq Filtering Analysis Removes low-quality cells from the outputs of the “Cell Ranger Count (RNA)”, “Cell Ranger Count (RNA+VDJ)”, and “Cell Ranger Aggregate (RNA, RNA+VDJ)” pipelines. The results of this workflow are used in the “Single-Cell RNA-Seq Dimensionality Reduction Analysis” pipeline.

 https://github.com/datirium/workflows.git

Path: workflows/sc-rna-filter.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869
workflow graph heatmap-prepare.cwl

Workflow runs homer-make-tag-directory.cwl tool using scatter for the following inputs - bam_file - fragment_size - total_reads `dotproduct` is used as a `scatterMethod`, so one element will be taken from each array to construct each job: 1) bam_file[0] fragment_size[0] total_reads[0] 2) bam_file[1] fragment_size[1] total_reads[1] ... N) bam_file[N] fragment_size[N] total_reads[N] `bam_file`, `fragment_size` and `total_reads` arrays should have the identical order.

https://github.com/Barski-lab/workflows.git

Path: tools/heatmap-prepare.cwl

Branch/Commit ID: 2d54a7edc45b7dfbee41ecef200a634fd0cd5e97
workflow graph conflict-wf.cwl#collision

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/conflict-wf.cwl

Branch/Commit ID: 10492acee927c177933160f6ad67085f9112b0d1

Packed ID: collision
workflow graph Variant calling germline paired-end

A workflow for the Broad Institute's best practices gatk4 germline variant calling pipeline. ## __Outputs__ #### Primary Output files: - bqsr2_indels.vcf, filtered and recalibrated indels (IGV browser) - bqsr2_snps.vcf, filtered and recalibrated snps (IGV browser) - bqsr2_snps.ann.vcf, filtered and recalibrated snps with effect annotations #### Secondary Output files: - sorted_dedup_reads.bam, sorted deduplicated alignments (IGV browser) - raw_indels.vcf, first pass indel calls - raw_snps.vcf, first pass snp calls #### Reports: - overview.md (input list, alignment metrics, variant counts) - insert_size_histogram.pdf - recalibration_plots.pdf - snpEff_summary.html ## __Inputs__ #### General Info - Sample short name/Alias: unique name for sample - Experimental condition: condition, variable, etc name (e.g. \"control\" or \"20C 60min\") - Cells: name of cells used for the sample - Catalog No.: vender catalog number if available - BWA index: BWA index sample that contains reference genome FASTA with associated indices. - SNPEFF database: Name of SNPEFF database to use for SNP effect annotation. - Read 1 file: First FASTQ file (generally contains \"R1\" in the filename) - Read 2 file: Paired FASTQ file (generally contains \"R2\" in the filename) #### Advanced - Ploidy: number of copies per chromosome (default should be 2) - SNP filters: see Step 6 Notes: https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/ - Indel filters: see Step 7 Notes: https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/ #### SNPEFF notes: Get snpeff databases using `docker run --rm -ti gatk4-dev /bin/bash` then running `java -jar $SNPEFF_JAR databases`. Then, use the first column as SNPEFF input (e.g. \"hg38\"). - hg38, Homo_sapiens (USCS), http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_hg38.zip - mm10, Mus_musculus, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_mm10.zip - dm6.03, Drosophila_melanogaster, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_dm6.03.zip - Rnor_6.0.86, Rattus_norvegicus, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_Rnor_6.0.86.zip - R64-1-1.86, Saccharomyces_cerevisiae, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_R64-1-1.86.zip ### __Data Analysis Steps__ 1. Trimming the adapters with TrimGalore. - This step is particularly important when the reads are long and the fragments are short - resulting in sequencing adapters at the ends of reads. If adapter is not removed the read will not map. TrimGalore can recognize standard adapters, such as Illumina or Nextera/Tn5 adapters. 2. Generate quality control statistics of trimmed, unmapped sequence data 3. Run germline variant calling pipeline, custom wrapper script implementing Steps 1 - 17 of the Broad Institute's best practices gatk4 germline variant calling pipeline (https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/) ### __References__ 1. https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/ 2. https://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels- 3. https://software.broadinstitute.org/software/igv/VCF

https://github.com/datirium/workflows.git

Path: workflows/vc-germline-pe.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869