Explore Workflows

View already parsed workflows here or click here to add your own

Graph Name Retrieved From View
workflow graph facets-workflow.cwl

Workflow for running Facets-suite on a set of tumor normal pairs This workflow scatters over all the pairs in the input JSON to run all samples in parallel Input JSON format ----------------- { \"pairs\": [ { \"tumor_bam\": { \"class\": \"File\", \"path\": \"/test_data/bam/Tumor1.rg.md.abra.printreads.bam\" }, \"normal_bam\": { \"class\": \"File\", \"path\": \"/test_data/bam/Normal1.rg.md.abra.printreads.bam\" }, \"pair_maf\": { \"class\": \"File\", \"path\": \"/test_data/bam/Tumor1.Normal1.maf\" }, \"pair_id\": \"Tumor1.Normal1\" }, { \"tumor_bam\": { \"class\": \"File\", \"path\": \"/test_data/bam/Tumor2.rg.md.abra.printreads.bam\" }, \"normal_bam\": { \"class\": \"File\", \"path\": \"/test_data/bam/Normal2.rg.md.abra.printreads.bam\" }, \"pair_maf\": { \"class\": \"File\", \"path\": \"/test_data/bam/Tumor2.Normal2.maf\" }, \"pair_id\": \"Tumor2.Normal2\" } ] } Output format ------------- output └── facets-suite ├── Tumor1.Normal1.arm_level.txt ├── Tumor1.Normal1.gene_level.txt ├── Tumor1.Normal1_hisens.ccf.maf ├── Tumor1.Normal1_hisens.rds ├── Tumor1.Normal1_hisens.seg ├── Tumor1.Normal1_purity.rds ├── Tumor1.Normal1_purity.seg ├── Tumor1.Normal1.qc.txt ├── Tumor1.Normal1.snp_pileup.gz ├── Tumor1.Normal1.txt ├── Tumor2.Normal2.arm_level.txt ├── Tumor2.Normal2.gene_level.txt ├── Tumor2.Normal2_hisens.ccf.maf ├── Tumor2.Normal2_hisens.rds ├── Tumor2.Normal2_hisens.seg ├── Tumor2.Normal2_purity.rds ├── Tumor2.Normal2_purity.seg ├── Tumor2.Normal2.qc.txt ├── Tumor2.Normal2.snp_pileup.gz ├── Tumor2.Normal2.txt └── logs ├── success └── failed

https://github.com/mskcc/pluto-cwl.git

Path: cwl/facets-workflow.cwl

Branch/Commit ID: 5cad957fec135aa55ca8d588372db0557ca1cad5
workflow graph Tumor-Only Detect Variants workflow

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

Path: definitions/pipelines/tumor_only_detect_variants.cwl

Branch/Commit ID: 788bdc99c1d5b6ee7c431c3c011eb30d385c1370
workflow graph SoupX Estimate

SoupX Estimate ==============

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

Path: workflows/soupx.cwl

Branch/Commit ID: c9e7f3de7f6ba38ee663bd3f9649e8d7dbac0c86
workflow graph alignment for nonhuman with qc

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

Path: definitions/pipelines/alignment_wgs_nonhuman.cwl

Branch/Commit ID: 1750cd5cc653f058f521b6195e3bec1e7df1a086
workflow graph Cell Ranger Aggregate

Cell Ranger Aggregate =====================

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

Path: workflows/cellranger-aggr.cwl

Branch/Commit ID: 60854b5d299df91e135e05d02f4be61f6a310fbc
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: 479c9b3e3fa32ec9c7cd4073cfbccc675fd254d9
workflow graph bwa_mem

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/subworkflows/bwa_mem.cwl

Branch/Commit ID: 3f6a871f81f343cf81a345f73ff2eeac70804b8c
workflow graph Cut-n-Run pipeline paired-end

Experimental pipeline for Cut-n-Run analysis. Uses mapping results from the following experiment types: - `chipseq-pe.cwl` - `trim-chipseq-pe.cwl` - `trim-atacseq-pe.cwl` Note, the upstream analyses should not have duplicates removed

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

Path: workflows/trim-chipseq-pe-cut-n-run.cwl

Branch/Commit ID: c9e7f3de7f6ba38ee663bd3f9649e8d7dbac0c86
workflow graph wgs alignment and germline variant detection

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

Path: definitions/pipelines/germline_wgs.cwl

Branch/Commit ID: 174f3b239018328cec1d821947438b457552724c
workflow graph Motif Finding with HOMER with target and background regions from peaks

Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis-peak.cwl

Branch/Commit ID: 4a5c59829ff8b9f3c843e66e3c675dcd9c689ed5