Explore Workflows

https://github.com/kids-first/kf-alignment-workflow.git

Path: subworkflows/untar_reference_tar.cwl

Branch/Commit ID: 09c05e788df9cd77cead39892fd02140faa765a8

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

Path: definitions/subworkflows/vcf_eval_concordance.cwl

Branch/Commit ID: 389f6edccab082d947bee9c032f59dbdf9f7c325

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

Path: workflows/ChIP-Seq/diffbind-parallel.cwl

Branch/Commit ID: 33123d6a92bf0038951820d0d2c9cf501ae2ebf6

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: 1f03ff02ef829bdb9d582825bcd4ca239e84ca2e

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

Path: tests/wf/scatter2.cwl

Branch/Commit ID: 814bd0405a7701efc7d63e8f0179df394c7766f7

RNA-seq 04 quantification

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

Path: v1.0/RNA-seq_pipeline/04-quantification-se-stranded.cwl

Branch/Commit ID: ebd63f705d0fde7290e42c8300d5420c25cfbfe3

https://github.com/arvados/arvados-tutorial.git

Path: WGS-processing/cwl/wgs-processing-wf.cwl

Branch/Commit ID: d147d1d1fafeeea06bd09d9479337b0f5aab43b0

CWL version of the md_list.cwl workflow for HPC. This performs a system setup and runs a molecular dynamics simulation on the structure passed to this workflow. This workflow uses the md_gather.cwl sub-workflow to gather the outputs together to return these. To work with more than one structure this workflow can be called from either the md_launch.cwl workflow, or the md_launch_mutate.cwl workflow. These use scatter for parallelising the workflow. md_launch.cwl operates on a list of individual input molecule files. md_launch_mutate.cwl operates on a single input molecule file, and a list of mutations to apply to that molecule. Within that list of mutations, a value of 'WT' will indicate that the molecule should be simulated without any mutation being applied.

https://github.com/douglowe/biobb_hpc_cwl_md_list.git

Path: md_list.cwl

Branch/Commit ID: 97122f21048a5ac4a12b21059b751d1d07050cbd

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: 1f03ff02ef829bdb9d582825bcd4ca239e84ca2e

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

Path: definitions/subworkflows/pvacseq.cwl

Branch/Commit ID: 3a822294da63b4e19446a285e2fef075e23cf3d0