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Graph Name Retrieved From View
workflow graph Convert FastJs to npy arrays for gVCF input

https://github.com/curoverse/l7g.git

Path: cwl-version/masterworkflow/fastj2npy-wf.cwl

Branch/Commit ID: cdfe9178ad4e481d2391cd2da74b82d66a61b0bb
workflow graph Create NumPy arrays by tile path from cgfs, merge all NumPy arrays into single array

https://github.com/curoverse/l7g.git

Path: cwl-version/npy/createnpy-wf.cwl

Branch/Commit ID: cdfe9178ad4e481d2391cd2da74b82d66a61b0bb
workflow graph mutect parallel workflow

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

Path: definitions/subworkflows/mutect.cwl

Branch/Commit ID: 1750cd5cc653f058f521b6195e3bec1e7df1a086
workflow graph Workflow to validate the the gVCF to cgf conversion

https://github.com/curoverse/l7g.git

Path: cwl-version/checks/check-cgf/gvcf/check-cgf-gvcf-wf.cwl

Branch/Commit ID: cdfe9178ad4e481d2391cd2da74b82d66a61b0bb
workflow graph import_include_test.cwl

https://github.com/wshands/CWL1.2Test.git

Path: import_include_test.cwl

Branch/Commit ID: c6b569882d4791ae28df4ee3b07a443e41fbff26
workflow graph taxonomy_check_16S

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 0514ffe248dd11068a3f2268bc67b6ce5ab051d2
workflow graph gcaccess_from_list

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

Path: task_types/tt_gcaccess_from_list.cwl

Branch/Commit ID: 33414c888997d558bdcb558ca33c3a728a3e6143
workflow graph process VCF workflow

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

Path: definitions/subworkflows/strelka_process_vcf.cwl

Branch/Commit ID: 77ec4f26eb14ed82481828bd9f6ef659cfd8b40f
workflow graph Cellranger reanalyze - reruns secondary analysis performed on the feature-barcode matrix

Devel version of Single-Cell Cell Ranger Reanalyze ================================================== Workflow calls \"cellranger aggr\" command to rerun secondary analysis performed on the feature-barcode matrix (dimensionality reduction, clustering and visualization) using different parameter settings. As an input we use filtered feature-barcode matrices in HDF5 format from cellranger count or aggr experiments. Note, we don't pass aggregation_metadata from the upstream cellranger aggr step. Need to address this issue when needed.

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

Path: workflows/cellranger-reanalyze.cwl

Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4
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: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4