Explore Workflows

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

Graph Name Retrieved From View
workflow graph Apply filters to VCF file

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

Path: definitions/subworkflows/filter_vcf.cwl

Branch/Commit ID: ec45fad68ca10fb64d5c58e704991b146dc31d28
workflow graph tt_fscr_calls_pass1

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

Path: task_types/tt_fscr_calls_pass1.cwl

Branch/Commit ID: f5c11df465aaadf712c38ba4933679fe1cbe03ca
workflow graph varscan somatic workflow

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

Path: definitions/subworkflows/varscan.cwl

Branch/Commit ID: 3ee63d8757c341ca98b3b46ec4782862ad19b710
workflow graph protein_extract

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

Path: progs/protein_extract.cwl

Branch/Commit ID: 8ea3637b0f11eac1ea5599c41d74e00d85fb778d
workflow graph Unaligned BAM to BQSR and VCF

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

Path: definitions/subworkflows/bam_to_bqsr_no_dup_marking.cwl

Branch/Commit ID: 049f4aeff4c4a1b8421cac9b1c1c1f0da5848315
workflow graph Unaligned BAM to BQSR and VCF

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

Path: definitions/subworkflows/bam_to_bqsr_no_dup_marking.cwl

Branch/Commit ID: 051074fce4afd9732ef34db9dd43d3a1d8e979d6
workflow graph Generate genome index STAR RNA

Workflow makes indices for [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886). It performs the following steps: 1. Runs `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. Transforms array of files into [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates *chrNameLength.txt* file as an output

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

Path: workflows/star-index.cwl

Branch/Commit ID: e238d1756f1db35571e84d72e1699e5d1540f10c
workflow graph cond-wf-013.cwl

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/conditionals/cond-wf-013.cwl

Branch/Commit ID: e62f99dd79d6cb9c157cceb458f74200da84f6e9
workflow graph checker-workflow-wrapping-tool.cwl

This demonstrates how to wrap a \"real\" tool with a checker workflow that runs both the tool and a tool that performs verification of results

https://github.com/dockstore-testing/md5sum-checker.git

Path: checker-workflow-wrapping-tool.cwl

Branch/Commit ID: 761499a8329b367d37eb83d180fb762e04ada97f
workflow graph GSEApy - Gene Set Enrichment Analysis in Python

GSEAPY: Gene Set Enrichment Analysis in Python ============================================== Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA requires as input an expression dataset, which contains expression profiles for multiple samples. While the software supports multiple input file formats for these datasets, the tab-delimited GCT format is the most common. The first column of the GCT file contains feature identifiers (gene ids or symbols in the case of data derived from RNA-Seq experiments). The second column contains a description of the feature; this column is ignored by GSEA and may be filled with “NA”s. Subsequent columns contain the expression values for each feature, with one sample's expression value per column. It is important to note that there are no hard and fast rules regarding how a GCT file's expression values are derived. The important point is that they are comparable to one another across features within a sample and comparable to one another across samples. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA.

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

Path: workflows/gseapy.cwl

Branch/Commit ID: b5e16e359007150647b14dc6e038f4eb8dccda79