Explore Workflows

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

Path: definitions/subworkflows/pindel_region.cwl

Branch/Commit ID: 1750cd5cc653f058f521b6195e3bec1e7df1a086

STARR-seq 04 quantification

https://github.com/Duke-GCB/GGR-cwl.git

Path: v1.0/STARR-seq_pipeline/04-quantification.cwl

Branch/Commit ID: 487af88ef0b971f76ecd1a215639bb47e3ee94e1

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

Path: task_types/tt_kmer_build_tree.cwl

Branch/Commit ID: 22ffe27d9d4a899def7592d75d5871c1856adbdb

https://github.com/fhembroff/wes-testing.git

Path: manyjobs/manyjobs.cwl

Branch/Commit ID: 9db21180ef17cb71100386bdfafbf06f4cda441f

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

Path: definitions/subworkflows/bam_to_bqsr.cwl

Branch/Commit ID: 051074fce4afd9732ef34db9dd43d3a1d8e979d6

https://github.com/NCI-GDC/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/extract_amplicon_kit.cwl

Branch/Commit ID: 20a901f44c9fb0e6f4ee3c40ec33fa4b1c8ef005

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

Path: task_types/tt_align_sort_sa.cwl

Branch/Commit ID: 8a8fffb78b1e327ba0da51840ac8acc0c218d611

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/wf-loadContents.cwl

Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5

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: 2caa50434966ebdf4b33e5ca689c2e4df32f9058

https://github.com/arvados/arvados-tutorial.git

Path: WGS-processing/cwl/helper/bwamem-gatk-report-wf.cwl

Branch/Commit ID: 2691061efa8341166ad6518688e5e6c0fb9a8fbf