Explore Workflows

View already parsed workflows here or click here to add your own

Graph Name Retrieved From View
workflow graph fail-unconnected.cwl

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

Path: tests/fail-unconnected.cwl

Branch/Commit ID: 50251ef931d108c09bed2d330d3d4fe9c562b1c3
workflow graph Subworkflow to allow calling different SV callers which require bam files as inputs

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

Path: definitions/subworkflows/single_sample_sv_callers.cwl

Branch/Commit ID: 195b4ab487c939eb32a55d9f78bc1befd100caae
workflow graph kallisto_wf_pe.cwl

https://github.com/pitagora-network/pitagora-cwl.git

Path: workflows/kallisto/paired_end/kallisto_wf_pe.cwl

Branch/Commit ID: 14c76e15665ef0566eae5c2be75dba7feb74a50b
workflow graph trick_revsort.cwl

Reverse the lines in a document, then sort those lines.

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

Path: tests/wf/trick_revsort.cwl

Branch/Commit ID: 7dec97bb8f0bc2d9e9eb710faf41f2e98cc7cdda
workflow graph FASTQ to BQSR

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

Path: definitions/subworkflows/fastq_to_bqsr.cwl

Branch/Commit ID: 77ec4f26eb14ed82481828bd9f6ef659cfd8b40f
workflow graph Motif Finding with HOMER with custom background regions

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: a0b22644ca178b640fb74849d23b7c631022f0b5
workflow graph cram_to_bam workflow

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

Path: definitions/subworkflows/cram_to_bam_and_index.cwl

Branch/Commit ID: 195b4ab487c939eb32a55d9f78bc1befd100caae
workflow graph bam to trimmed fastqs and biscuit alignments

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

Path: definitions/subworkflows/bam_to_trimmed_fastq_and_biscuit_alignments.cwl

Branch/Commit ID: 195b4ab487c939eb32a55d9f78bc1befd100caae
workflow graph Motif Finding with HOMER with random background regions

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
workflow graph count-lines2-wf.cwl

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

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

Branch/Commit ID: 4fe434e969c93c94b690ba72db295d9d52a6f576