Explore Workflows

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

Graph Name Retrieved From View
workflow graph tRNA_selection.cwl

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

Path: tools/tRNA_selection.cwl

Branch/Commit ID: 71d9c83761ea301a895dd669902979ef5a4b279b
workflow graph count-lines12-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/count-lines12-wf.cwl

Branch/Commit ID: 227f35a5ed50c423afba2353871950aa61d58872
workflow graph count-lines8-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/count-lines8-wf.cwl

Branch/Commit ID: fd6e054510e2bb65eed4069a3a88013d7ecbb99c
workflow graph io-any-wf-1.cwl

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

Path: tests/io-any-wf-1.cwl

Branch/Commit ID: a0f2d38e37ff51721fdeaf993bb2ab474b17246b
workflow graph qc_workflow_wo_waltz.cwl

This workflow is intended to be used to test the QC module, without having to run the long waltz step

https://github.com/mskcc/Innovation-Pipeline.git

Path: workflows/QC/qc_workflow_wo_waltz.cwl

Branch/Commit ID: 9998da2da694af2edad7c2135f6995e2282794a3
workflow graph scatter-valuefrom-wf4.cwl#main

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

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

Branch/Commit ID: a3d565bf8e630101d25d31804cfbceb0a0ba28de

Packed ID: main
workflow graph sum-wf.cwl

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

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

Branch/Commit ID: 280a852e74aec08cf79687e8004e17b1ab464534
workflow graph scatter-valuefrom-wf4.cwl#main

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

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

Branch/Commit ID: 9e7c68c0834645ba53a7e2b5f70d53df9d051c92

Packed ID: main
workflow graph qc_workflow.cwl

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/QC/qc_workflow.cwl

Branch/Commit ID: 9e6eae9eb8448e68d509397a46303551a93a164d
workflow graph PCA - Principal Component Analysis

Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy.

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

Path: workflows/pca.cwl

Branch/Commit ID: 4a5c59829ff8b9f3c843e66e3c675dcd9c689ed5