Explore Workflows

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

Path: v1.0/v1.0/scatter-wf4.cwl

Branch/Commit ID: 622134ebc48980676b7e53fe39405c428920c03e

Packed ID: main

Animation of an oil spill with openoil

https://github.com/ILIAD-ocean-twin/application_package.git

Path: openoil/pipeline.cwl

Branch/Commit ID: cb62ab53f349bf64e880199d1e148439ebe456c1

Packed ID: openoil_pipeline

Reverse the lines in a document, then sort those lines.

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

Path: cwltool/schemas/v1.0/v1.0/revsort.cwl

Branch/Commit ID: 6e9f82a6d2195d4f16f28fd6e1485138372fb430

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf4.cwl

Branch/Commit ID: fd6e054510e2bb65eed4069a3a88013d7ecbb99c

Packed ID: main

Cell Ranger Aggregate (RNA+ATAC) Combines outputs from multiple runs of “Cell Ranger Count (RNA+ATAC)” pipeline. The results of this workflow are primarily used in “Single-Cell Multiome ATAC and RNA-Seq Filtering Analysis” pipeline.

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

Path: workflows/cellranger-arc-aggr.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

Cell Ranger ARC Count Gene Expression + ATAC ============================================

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

Path: workflows/cellranger-arc-count.cwl

Branch/Commit ID: 2caa50434966ebdf4b33e5ca689c2e4df32f9058

https://github.com/hubmapconsortium/sc-atac-seq-pipeline.git

Path: steps/snaptools_create_snap_file.cwl

Branch/Commit ID: bb023f95ca3330128bfef41cc719ffcb2ee6a190

https://github.com/kathy-t/ghapps-single-workflow.git

Path: Dockstore.cwl

Branch/Commit ID: b9eb2dcd745bf3cdd6e9c3396a507e11d60c87fe

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

Path: definitions/pipelines/panel_of_normals.cwl

Branch/Commit ID: ddb49a0951d9ad537269d7db3fe8f904495a8bf4

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: 12e5256de1b680c551c87fd5db6f3bc65428af67