Explore Workflows

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

Graph Name Retrieved From View
workflow graph Identification_workflow.cwl

https://github.com/adamscharlotte/CWL-workflow.git

Path: Identification_workflow.cwl

Branch/Commit ID: f165c14d6d2e20a5133fc42251ae565fcbe520f6
workflow graph exome alignment and germline variant detection

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

Path: definitions/pipelines/germline_exome.cwl

Branch/Commit ID: 25eab0390f6866ce491b44c89d9e0435d228ab6f
workflow graph rnaseq-star-rsem-pe.cwl

https://github.com/pitagora-network/DAT2-cwl.git

Path: workflow/rna-seq/rnaseq-star-rsem-pe/rnaseq-star-rsem-pe.cwl

Branch/Commit ID: b567c5d9f9cc5f7b06f187611ae94d6daeaf3613
workflow graph Exome QC workflow

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

Path: definitions/subworkflows/qc_exome_no_verify_bam.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8
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: ef7f3345b352319ec22dffba26c79df033b141f9
workflow graph Filter single sample sv vcf from depth callers(cnvkit/cnvnator)

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

Path: definitions/subworkflows/sv_depth_caller_filter.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8
workflow graph star-cufflinks_wf_se.cwl

https://github.com/pitagora-network/pitagora-cwl.git

Path: workflows/star-cufflinks/single_end/star-cufflinks_wf_se.cwl

Branch/Commit ID: 14c76e15665ef0566eae5c2be75dba7feb74a50b
workflow graph fp_filter workflow

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

Path: definitions/subworkflows/fp_filter.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8
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: 480e99a4bb3046e0565113d9dca294e0895d3b0c
workflow graph RNA-Seq alignment and transcript/gene abundance workflow

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

Path: definitions/pipelines/rnaseq.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8