Explore Workflows

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

Path: definitions/subworkflows/bam_to_trimmed_fastq_and_biscuit_alignments.cwl

Branch/Commit ID: 195b4ab487c939eb32a55d9f78bc1befd100caae

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: a0b22644ca178b640fb74849d23b7c631022f0b5

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

Path: v1.0/v1.0/count-lines2-wf.cwl

Branch/Commit ID: 4fe434e969c93c94b690ba72db295d9d52a6f576

ATAC-seq 01 QC - reads: PE

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

Path: v1.0/ATAC-seq_pipeline/01-qc-pe.cwl

Branch/Commit ID: 67e8ccd5abddbd9e27f23ceeb95536fecf792d93

Filters ChIP/ATAC peaks with the neatest genes assigned for Tag Density Profile or Motif Enrichment analyses ============================================================================================================ Tool filters output from any ChIP/ATAC pipeline to create a file with regions of interest for Tag Density Profile or Motif Enrichment analyses. Peaks with duplicated coordinates are discarded.

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

Path: workflows/filter-peaks-for-heatmap.cwl

Branch/Commit ID: 9e3c3e65c19873cd1ed3cf7cc3b94ebc75ae0cc5

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

Path: definitions/subworkflows/bam_to_trimmed_fastq_and_hisat_alignments.cwl

Branch/Commit ID: 3a822294da63b4e19446a285e2fef075e23cf3d0

This workflow creates tagAlign file

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

Path: workflows/File-formats/create-tagAlign.cwl

Branch/Commit ID: 3247592a89deafaa0d9c5910a1cb1d000ef9b098

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

Path: _includes/cwl/21-1st-workflow/1st-workflow.cwl

Branch/Commit ID: 3a59e37dc3de19dbbb9b39015f30c459f667ae12

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

Path: definitions/subworkflows/qc_exome.cwl

Branch/Commit ID: 061d3a2fbcd8a1c39c0b38c549e528deb24a9d54

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

Path: tests/wf/1st-workflow.cwl

Branch/Commit ID: 7dec97bb8f0bc2d9e9eb710faf41f2e98cc7cdda