Explore Workflows

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

Path: task_types/tt_align_merge_sas.cwl

Branch/Commit ID: cec32f5b60c1d048257e3c3daed6912d5d2a054e

Super-enhancers, consist of clusters of enhancers that are densely occupied by the master regulators and Mediator. Super-enhancers differ from typical enhancers in size, transcription factor density and content, ability to activate transcription, and sensitivity to perturbation. Use to create stitched enhancers, and to separate super-enhancers from typical enhancers using sequencing data (.bam) given a file of previously identified constituent enhancers (.gff)

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

Path: workflows/super-enhancer.cwl

Branch/Commit ID: 7eef0294395d83ff0765fce61726a59d71126422

https://github.com/bioexcel/biobb_wf_cwl_tutorial.git

Path: biobb_wf_cwl_tutorial/examples/BioExcel-CWL-firstWorkflow.cwl

Branch/Commit ID: 6f5b88c636dc2fb1d8a85188d467d1c89113a931

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

Path: bacterial_ncrna/wf_gcmsearch.cwl

Branch/Commit ID: 2afb5ebafd1353ba063cc74ee9a7eaf347afce5c

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

Path: definitions/pipelines/immuno.cwl

Branch/Commit ID: a670f323e77e02d9b77be9a13d73d5276dd3676c

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

Path: workflows/ChIP-Seq/idr.cwl

Branch/Commit ID: 0207b0171ab142dfb85db9c39050c5b4be51dd9e

Workflow makes indices for [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886). It performs the following steps: 1. Runs `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. Transforms array of files into [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates *chrNameLength.txt* file as an output

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

Path: workflows/star-index.cwl

Branch/Commit ID: d6f58c383d0676269afb519399061191a1144a6a

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: 7ced5a5259dbd8b3fc64456beaeffd44f4a24081

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

Path: tests/steplevel-resreq.cwl

Branch/Commit ID: c7c97715b400ff2194aa29fc211d3401cea3a9bf

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

Path: tests/count-lines7-wf.cwl

Branch/Commit ID: 1f3ef888d9ef2306c828065c460c1800604f0de4