Explore Workflows

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Graph Name Retrieved From View
workflow graph Create Genomic Collection for Bacterial Pipeline

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

Path: genomic_source/wf_genomic_source.cwl

Branch/Commit ID: 9362082213e20315f76f6f5c235cac3aae565747
workflow graph standard_pipeline.cwl

This is a workflow to go from UMI-tagged fastqs to standard bams. It does not include collapsing, or QC It does include modules 1 and 2

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/standard_pipeline.cwl

Branch/Commit ID: 9998da2da694af2edad7c2135f6995e2282794a3
workflow graph workflow.cwl

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: flow_dispatch/2blat/workflow.cwl

Branch/Commit ID: f7894707dd30a0edd199d3b67c4c8678f64c90b3
workflow graph assemble.cwl

Assemble a set of reads using SKESA

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

Path: assemble.cwl

Branch/Commit ID: 22ffe27d9d4a899def7592d75d5871c1856adbdb
workflow graph Exome QC workflow

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

Path: definitions/subworkflows/qc_exome.cwl

Branch/Commit ID: ffd73951157c61c1581d346628d75b61cdd04141
workflow graph tt_univec_wnode.cwl

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

Path: task_types/tt_univec_wnode.cwl

Branch/Commit ID: 449f87c8365637e803ba66f83367e96f98c88f5c
workflow graph kmer_cache_retrieve

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

Path: task_types/tt_kmer_cache_retrieve.cwl

Branch/Commit ID: f5c11df465aaadf712c38ba4933679fe1cbe03ca
workflow graph HBA_target.cwl

https://git.astron.nl/RD/LINC.git

Path: workflows/HBA_target.cwl

Branch/Commit ID: 9ead9ff182f8233ffd908f72aa3b3ff516aefd9d
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: 2c486543c335bb99b245dfe7e2f033f535efb9cf
workflow graph revsort.cwl

Reverse the lines in a document, then sort those lines.

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

Path: tests/wf/revsort.cwl

Branch/Commit ID: 5ae5798f1c0c8d2178986b77cfd74edff510877a