Explore Workflows

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

Graph Name Retrieved From View
workflow graph revsort.cwl

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

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

Path: tests/wf/revsort.cwl

Branch/Commit ID: 4700fbee9a5a3271eef8bc9ee595619d0720431b
workflow graph RNASelector as a CWL workflow

https://doi.org/10.1007/s12275-011-1213-z

 https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git

Path: workflows/rna-selector.cwl

Branch/Commit ID: 3f85843d4a6debdabe96bc800bf2a4efdcda1ef3
workflow graph bam to trimmed fastqs and biscuit alignments

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

Path: definitions/subworkflows/bam_to_trimmed_fastq_and_biscuit_alignments.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8
workflow graph EMG assembly for paired end Illumina

https://github.com/proteinswebteam/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v4-assembly-metaSPAdes.cwl

Branch/Commit ID: 7bb76f33bf40b5cd2604001cac46f967a209c47f
workflow graph wf-alignment.cwl

https://github.com/farahzkhan/bcbio_test_cwlprov.git

Path: somatic/somatic-workflow/wf-alignment.cwl

Branch/Commit ID: 7c46d5c6ef6501dc0e07a9b740e9de64ffec83f5
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: 581156366f91861bd4dbb5bcb59f67d468b32af3
workflow graph EMG pipeline v3.0 (paired end version)

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v3-paired.cwl

Branch/Commit ID: a8abd0e66de7b5ffe24cfe7f39d7027103c6d3b4
workflow graph EMG pipeline v4.0 (single end version)

https://github.com/proteinswebteam/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v4-single.cwl

Branch/Commit ID: ecf044f3a5a7589cb2238487a19f22863c2bcdb1
workflow graph Detect Variants workflow for WGS pipeline

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

Path: definitions/pipelines/detect_variants_wgs.cwl

Branch/Commit ID: 97572e3a088d79f6a4166385f79e79ea77b11470
workflow graph Detect DoCM variants

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

Path: definitions/subworkflows/docm_germline.cwl

Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d