Explore Workflows

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

Graph Name Retrieved From View
workflow graph Run pindel on provided region

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

Path: definitions/subworkflows/pindel_region.cwl

Branch/Commit ID: 389f6edccab082d947bee9c032f59dbdf9f7c325
workflow graph cram_to_bam workflow

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

Path: definitions/subworkflows/cram_to_bam_and_index.cwl

Branch/Commit ID: 35e6b3ef71b4a2a9caba1dbd5dc424a8809bcc0a
workflow graph mutect parallel workflow

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

Path: definitions/subworkflows/mutect.cwl

Branch/Commit ID: 2decd55996b912feb48be5db1b052aa3274ee405
workflow graph Merge, annotate, and generate a TSV for SVs

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

Path: definitions/subworkflows/merge_svs.cwl

Branch/Commit ID: 35e6b3ef71b4a2a9caba1dbd5dc424a8809bcc0a
workflow graph align_merge_sas

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

Path: task_types/tt_align_merge_sas.cwl

Branch/Commit ID: f1eb0f4eaaf1661044f28d859f7e8d4302525ead
workflow graph align_sort_sa

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

Path: task_types/tt_align_sort_sa.cwl

Branch/Commit ID: f1eb0f4eaaf1661044f28d859f7e8d4302525ead
workflow graph umi molecular alignment workflow

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

Path: definitions/subworkflows/molecular_qc.cwl

Branch/Commit ID: 480c438a6a7e78c624712aec01bc4214d2bc179c
workflow graph realignment.cwl

https://github.com/mskcc/argos-cwl.git

Path: modules/pair/realignment.cwl

Branch/Commit ID: 46eddf1e191352cad5e95dd3c24eeae3738da485
workflow graph mk_coverage_QC_from_bed.cwl

https://github.com/YinanWang16/tso500-ctdna-post-processing.git

Path: cwl/workflows/mk_coverage_QC_from_bed.cwl

Branch/Commit ID: 6e6a592621de7749e3753cb24daeca073bd0d6e2
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: 17a4a68b20e0af656e09714c1f39fe761b518686