Explore Workflows

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

Path: tests/scatter-valuefrom-wf6.cwl

Branch/Commit ID: ea9f8634e41824ac3f81c3dde698d5f0eef54f1b

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

Path: testdata/step-valuefrom3-wf_v1_0.cwl

Branch/Commit ID: 0ab1d42d10f7311bb4032956c4a6f3d2730d9507

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

Path: definitions/pipelines/bisulfite.cwl

Branch/Commit ID: a9133c999502acf94b433af8d39897e6c2cdf65f

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

Path: tests/count-lines13-wf.cwl

Branch/Commit ID: 1f3ef888d9ef2306c828065c460c1800604f0de4

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

Path: definitions/pipelines/germline_exome_gvcf.cwl

Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d

Runs RNA-Seq BioWardrobe basic analysis with pair-end data file.

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

Path: workflows/rnaseq-pe.cwl

Branch/Commit ID: b4b7b2e7e508be5eac639f9e323d141daf714c0d

curl will download a HTTP/HTTPS resource or file from a given URL, following any redirections.

https://github.com/stain/ro-index-paper.git

Path: code/data-gathering/workflows/zip-content-by-url.cwl

Branch/Commit ID: 32bbf476555b4dcf29fde8d2ef1717ce64c312f9

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

Path: testdata/scatter-wf3_v1_0.cwl

Branch/Commit ID: c1875d54dedc41b1d2fa08634dcf1caa8f1bc631

Packed ID: main

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: e99e80a2c19682d59947bde04a892d7b6d90091c

Cell Ranger Count (ATAC) Quantifies single-cell chromatin accessibility of the sequencing data from a single 10x Genomics library. The results of this workflow are used in either the “Single-Cell ATAC-Seq Filtering Analysis” or “Cell Ranger Aggregate (ATAC)” pipeline.

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

Path: workflows/cellranger-atac-count.cwl

Branch/Commit ID: 93b844a80f4008cc973ea9b5efedaff32a343895