Explore Workflows

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

Graph Name Retrieved From View
workflow graph strelka workflow

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

Path: definitions/subworkflows/strelka_and_post_processing.cwl

Branch/Commit ID: 00df82a529a58d362158110581e1daa28b4d7ecb
workflow graph scatter-wf3.cwl#main

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

Path: v1.0/v1.0/scatter-wf3.cwl

Branch/Commit ID: 4d06b9efd26c5813c13684ebcc95547bb75ddfcc

Packed ID: main
workflow graph pindel parallel workflow

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

Path: definitions/subworkflows/pindel.cwl

Branch/Commit ID: 509938802c5e42bb8084c6a5a26ab6425c60e69a
workflow graph wgs alignment and germline variant detection

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

Path: definitions/pipelines/germline_wgs.cwl

Branch/Commit ID: 742dbafb5fb103d8578f48a0576c14dd8dae3b2a
workflow graph cache_asnb_entries

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

Path: task_types/tt_cache_asnb_entries.cwl

Branch/Commit ID: 3384fa5776c183d33bef830696b6edc6ec55a292
workflow graph Bisulfite QC tools

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

Path: definitions/subworkflows/bisulfite_qc.cwl

Branch/Commit ID: 0b6e8fd8ead7644cf5398395b76af5cf4011686f
workflow graph count-lines11-wf.cwl

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

Path: v1.0/v1.0/count-lines11-wf.cwl

Branch/Commit ID: e67f19d8a713759d761ecad050966d1eb043b85c
workflow graph genotypegvcfs.cwl

https://github.com/uc-cdis/genomel_pipelines.git

Path: genomel/cwl/workflows/variant_calling/genotypegvcfs.cwl

Branch/Commit ID: 28bb82ba031041321ff9caa5c299ec1bb15d7471
workflow graph Running cellranger count and lineage inference

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

Path: definitions/subworkflows/single_cell_rnaseq.cwl

Branch/Commit ID: 9161ef43f7bf0e22b365fde9ec92edcb8601798e
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: b957a4f681bf0ca8ebba4e0d0ec3936bf79620c5