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Graph Name Retrieved From View
workflow graph trim-rnaseq-se-dutp.cwl

Runs RNA-Seq dUTP BioWardrobe basic analysis with strand specific single-end data file.

https://github.com/Barski-lab/workflows.git

Path: workflows/trim-rnaseq-se-dutp.cwl

Branch/Commit ID: ca2dbb71d0537b1d93a8bd44719250cf8949b157
workflow graph scatterfail.cwl

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

Path: tests/wf/scatterfail.cwl

Branch/Commit ID: 3ed10d0ea7ac57550433a89a92bdbe756bdb0e40
workflow graph iwdr-passthrough-successive.cwl

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

Path: tests/wf/iwdr-passthrough-successive.cwl

Branch/Commit ID: dbc4c4c2ad30ed31367b4fbcc3bb4084fdcabaa2
workflow graph Motif Finding with HOMER with random background regions

Motif Finding with HOMER with random 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. Here is how we generate background for Motifs Analysis ------------------------------------- 1. Take input file with regions in a form of “chr\" “start\" “end\" 2. Sort and remove duplicates from this regions file 3. Extend each region in 20Kb into both directions 4. Merge all overlapped extended regions 5. Subtract not extended regions from the extended ones 6. Randomly distribute not extended regions within the regions that we got as a result of the previous step 7. Get fasta file from these randomly distributed regions (from the previous step). Use it as background 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.cwl

Branch/Commit ID: 7030da528559c7106d156284e50ff0ecedab0c4e
workflow graph mut.cwl

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

Path: tests/wf/mut.cwl

Branch/Commit ID: 5ae5798f1c0c8d2178986b77cfd74edff510877a
workflow graph step_valuefrom5_wf_v1_2.cwl

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

Path: testdata/step_valuefrom5_wf_v1_2.cwl

Branch/Commit ID: 77669d4dd1d1ebd2bdd9810f911608146d9b8e51
workflow graph js-expr-req-wf.cwl#wf

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

Path: cwltool/schemas/v1.0/v1.0/js-expr-req-wf.cwl

Branch/Commit ID: 7c7615c44b80f8e76e659433f8c7875603ae0b25

Packed ID: wf
workflow graph assm_assm_blastn_wnode

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

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: 686b570a9fa46f3ace3f8e9935490b75df86a1fc
workflow graph wgs alignment and germline variant detection

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

Path: definitions/pipelines/germline_wgs.cwl

Branch/Commit ID: 8dc462a7d9ba1479f764682af99c69d8574cb3dc
workflow graph exome alignment and germline variant detection, with optitype for HLA typing

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

Path: definitions/pipelines/germline_exome_hla_typing.cwl

Branch/Commit ID: 3034168d652bfa930ba09af20e473a4564a8010d