Explore Workflows

Motif Finding with HOMER with custom background regions --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis-bg.cwl

Branch/Commit ID: 92f1a6da9c4f85fb51340b01b32373a50fde0891

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

Path: v1.0/v1.0/io-int-default-tool-and-wf.cwl

Branch/Commit ID: a5ae5ad0c9017ed625fb372f65e72dbb069439b0

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

Path: cwltool/schemas/v1.0/v1.0/echo-wf-default.cwl

Branch/Commit ID: 7bfd77118cdc80dd7150115dd7a1a7ee6046f6fe

AltAnalyze ICGS ===============

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

Path: workflows/altanalyze-icgs.cwl

Branch/Commit ID: 2005c6b7f1bff6247d015ff6c116bd9ec97158bb

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/conflict-wf.cwl

Branch/Commit ID: 50251ef931d108c09bed2d330d3d4fe9c562b1c3

Packed ID: collision

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

Path: tests/count-lines12-wf.cwl

Branch/Commit ID: a0f2d38e37ff51721fdeaf993bb2ab474b17246b

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: 10ce6e113f749c7bd725e426445220c3bdc5ddf1

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

Path: task_types/tt_blastn_wnode.cwl

Branch/Commit ID: b0ee40d34d233f1611c2e2c66b6d22a3b7deec05

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

Path: progs/gp_makeblastdb.cwl

Branch/Commit ID: b38b0070edf910984f29a4a495b5dfa525b8b305

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf4.cwl

Branch/Commit ID: 26870e38cec81af880cd3e4789ae6cee8fc27020

Packed ID: main