Explore Workflows

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: 17a4a68b20e0af656e09714c1f39fe761b518686

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

Path: workflows/dnaseq/transform.cwl

Branch/Commit ID: f34d3963b33e0a379338cb3cb75b0016f012bf2c

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

Path: task_types/tt_kmer_top_n.cwl

Branch/Commit ID: 16e3915d2a357e2a861b30911c832e5ddc0c1784

Workflow to merge a large number of maf files into a single consensus maf file for use with GetBaseCountsMultiSample

https://github.com/mskcc/pluto-cwl.git

Path: cwl/consensus_maf.cwl

Branch/Commit ID: 7eb2b0a4d37018142233d770595ac2e00376dab4

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

Path: tests/wf/default-dir5.cwl

Branch/Commit ID: d5f7fa162611243f0c66dd3e933c16a4964a09ca

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

Path: task_types/tt_kmer_cache_store.cwl

Branch/Commit ID: 68058b108cb5b0b72ebe244c42eefa2747e1d64a

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

Path: cwltool/schemas/v1.0/v1.0/dynresreq-workflow.cwl

Branch/Commit ID: 09323506da219ba3ddb5313bd83022b52cac9adc

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

Path: definitions/subworkflows/docm_germline.cwl

Branch/Commit ID: 60edaf6f57eaaf02cda1a3d8cb9a825aa64a43e2

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: f3e44d3b0f198cf5245c49011124dc3b6c2b06fd

### Devel version of QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data

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

Path: workflows/trim-quantseq-mrnaseq-se-strand-specific.cwl

Branch/Commit ID: 2caa50434966ebdf4b33e5ca689c2e4df32f9058