Explore Workflows

https://github.com/yyoshiaki/VIRTUS.git

Path: workflow/createindex_singlevirus.cwl

Branch/Commit ID: 49faf55f97c8f3084b426d2db6640519d6f2ce71

A workflow for running MuSe, MuTect, SomaticSniper, and Pindel. See [the github repository](https://github.com/BD2KGenomics/dockstore_workflow_snps) for details.

https://github.com/bd2kgenomics/dockstore_workflow_snps.git

Path: snp_callers_workflow.cwl

Branch/Commit ID: 8c8217b63d16c13924a00408e2b621d888b0aaa5

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

Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl

Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d

### Devel version of QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data

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

Path: workflows/trim-quantseq-mrnaseq-se-strand-specific.cwl

Branch/Commit ID: 8a92669a566589d80fde9d151054ffc220ed4ddd

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

Path: metadata/indices-header.cwl

Branch/Commit ID: 299980af3a5cbdb4edcb27331d4846418d386e00

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v3.cwl

Branch/Commit ID: b6d3aaf3fa6695061208c6cdca3d7881cc45400d

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: 60854b5d299df91e135e05d02f4be61f6a310fbc

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

Path: definitions/subworkflows/cram_to_bam_and_index.cwl

Branch/Commit ID: 479c9b3e3fa32ec9c7cd4073cfbccc675fd254d9

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v4-assembly-metaSPAdes.cwl

Branch/Commit ID: b6d3aaf3fa6695061208c6cdca3d7881cc45400d

https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git

Path: workflows/16S_taxonomic_analysis.cwl

Branch/Commit ID: 886df9de6713e06228d2560c40f451155a196383