Explore Workflows

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

Path: definitions/pipelines/alignment_wgs.cwl

Branch/Commit ID: 8dc462a7d9ba1479f764682af99c69d8574cb3dc

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

Path: task_types/tt_fscr_calls_pass1.cwl

Branch/Commit ID: 093b60e546237c06cfe7820d6ac8d66467e66725

https://github.com/PMCC-BioinformaticsCore/janis-pipelines.git

Path: janis_pipelines/wgs_somatic/cwl/tools/vardictSomaticVariantCaller_v0_1_0.cwl

Branch/Commit ID: b4550175be9d485d509c61d87fddf88a8bdb70c1

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

Path: tests/sum-wf-noET.cwl

Branch/Commit ID: 664835e83eb5e57eee18a04ce7b05fb9d70d77b7

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: 5561f7ee11dd74848680351411a19aa87b13d27b

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

Path: definitions/pipelines/germline_wgs.cwl

Branch/Commit ID: 051074fce4afd9732ef34db9dd43d3a1d8e979d6

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

Path: definitions/pipelines/umi_duplex_alignment.cwl

Branch/Commit ID: 6f9f8a2057c6a9f221a44559f671e87a75c70075

Genomic regions intersection and visualization ============================================== 1. Merges intervals within each of the filtered peaks files from ChIP/ATAC experiments 2. Overlaps merged intervals and assigns the nearest genes to them

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

Path: workflows/intervene.cwl

Branch/Commit ID: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf

Creates indices for: * [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) * [bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) It performs the following steps: 1. `STAR --runMode genomeGenerate` to generate indices, based on [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) and [GTF](http://mblab.wustl.edu/GTF2.html) input files, returns results as an array of files 2. Outputs indices as [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates *chrNameLength.txt* file from Directory output 4. `bowtie-build` to generate indices requires genome [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) file as input, returns results as a group of main and secondary files

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

Path: workflows/genome-indices.cwl

Branch/Commit ID: 9ee330737f4603e4e959ffe786fbb2046db70a00

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

Path: task_types/tt_blastn_wnode.cwl

Branch/Commit ID: 50d161364e2859ed5c95ef07c9f7234f1431cf31