Explore Workflows

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

Graph Name Retrieved From View
workflow graph EMG pipeline v3.0 (single end version)

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

Path: workflows/emg-pipeline-v3.cwl

Branch/Commit ID: 3f85843

workflow graph 1st-workflow.cwl

https://github.com/cnherrera/testCWL.git

Path: 1st-workflow.cwl

Branch/Commit ID: main

workflow graph SSU-from-tablehits.cwl

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

Path: tools/SSU-from-tablehits.cwl

Branch/Commit ID: d4e5e53

workflow graph binning.cwl

https://github.com/EBI-Metagenomics/CWL-binning.git

Path: workflows/binning.cwl

Branch/Commit ID: master

workflow graph TransDecoder 2 step workflow, running TransDecoder.LongOrfs (step 1) followed by TransDecoder.Predict (step2)

https://github.com/mscheremetjew/workflow-is-cwl.git

Path: workflows/TransDecoder-v5-wf-2steps.cwl

Branch/Commit ID: assembly

workflow graph fastqc-0-11-4-1.cwl

https://github.com/4dn-dcic/pipelines-cwl.git

Path: cwl_awsem_v1/fastqc-0-11-4-1.cwl

Branch/Commit ID: dev2

workflow graph hi-c-processing-pairs-nore.cwl

https://github.com/mr-c/4dn-dcic-pipelines-cwl.git

Path: cwl_awsem_v1/hi-c-processing-pairs-nore.cwl

Branch/Commit ID: master

workflow graph wf-variantcall.cwl

https://github.com/bcbio/bcbio_validation_workflows.git

Path: wes-agha-test/wes_chr21_test-workflow-arvados/wf-variantcall.cwl

Branch/Commit ID: master

workflow graph PrediXcan

Predict.py has been wrapped in cwl, getting the information from: https://github.com/hakyimlab/MetaXcan/wiki/Individual-level-PrediXcan:-introduction,-tutorials-and-manual Here is a snippet from: https://github.com/hakyimlab/MetaXcan/wiki/Individual-level-PrediXcan:-introduction,-tutorials-and-manual In the following, we focus on the individual-level implementation of PrediXcan. The method was originally implemented in this repository. PrediXcan consists of two steps: Predict gene expression (or whatever biology the models predict) in a cohort with available genotypes Run associations to a trait measured in the cohort The first step is implemented in Predict.py. The prediction models are trained and pre-compiled on specific data sets with their own human genome releases and variant definitions. We implemented a few rules to support variant matching from genotypes based on different variant definitions. In the following, mapping refers to the process of assigning a model variant to a genotype variant. Originally, PrediXcan was applied to genes so we say \"gene expression\" a lot as it was the mechanism we initially studied. But conceptually, everything said here applies to any intermediate/molecular mechanism such as splicing or brain morphology. Whenever we say \"gene\", it generally could mean a splicing intron event, etc.

https://github.com/cwl-apps/predixcan_tools.git

Path: predixcan/predixcan_unpack.cwl

Branch/Commit ID: main

workflow graph Create target and anti-target files for CNA analysis

https://github.com/ChrisMaherLab/PACT.git

Path: subworkflows/cnvkit_prep_regions.cwl

Branch/Commit ID: master