Explore Workflows

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

Path: v1.0/v1.0/sum-wf.cwl

Branch/Commit ID: 4d06b9efd26c5813c13684ebcc95547bb75ddfcc

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/extract_readgroups_bam_http.cwl

Branch/Commit ID: ff015418f870bdfbd82ba675eb549fe8b4584b0c

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

Path: tests/count-lines9-wf.cwl

Branch/Commit ID: 50251ef931d108c09bed2d330d3d4fe9c562b1c3

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

Path: protein_alignment/wf_compart_filter_prosplign.cwl

Branch/Commit ID: a402541b8530f30eab726c160da90a23036847a1

This workflow creates tagAlign file

https://github.com/ncbi/cwl-ngs-workflows-cbb.git

Path: workflows/File-formats/create-tagAlign.cwl

Branch/Commit ID: 0207b0171ab142dfb85db9c39050c5b4be51dd9e

https://github.com/GusEllerm/CWL_workflows.git

Path: workflows/BEAST_examples/beast-2step-workflow.cwl

Branch/Commit ID: 57ea92224b9a5b411060f9398ca0dbdc5829db23

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

Path: protein_alignment/wf_align_filter.cwl

Branch/Commit ID: a402541b8530f30eab726c160da90a23036847a1

ATAC-seq 02 trimming - reads: PE

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

Path: v1.0/ATAC-seq_pipeline/02-trim-pe.cwl

Branch/Commit ID: 67e8ccd5abddbd9e27f23ceeb95536fecf792d93

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: e45ab1b9ac5c9b99fdf7b3b1be396dc42c2c9620

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: e45ab1b9ac5c9b99fdf7b3b1be396dc42c2c9620