Explore Workflows
View already parsed workflows here or click here to add your own
| Graph | Name | Retrieved From | View |
|---|---|---|---|
|
|
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: 3042812447d9e8889c6118986490e9c9b9b13223 |
|
|
|
Cut-n-Run pipeline paired-end
Experimental pipeline for Cut-n-Run analysis. Uses mapping results from the following experiment types: - `chipseq-pe.cwl` - `trim-chipseq-pe.cwl` - `trim-atacseq-pe.cwl` Note, the upstream analyses should not have duplicates removed |
https://github.com/datirium/workflows.git
Path: workflows/trim-chipseq-pe-cut-n-run.cwl Branch/Commit ID: b957a4f681bf0ca8ebba4e0d0ec3936bf79620c5 |
|
|
|
tt_hmmsearch_wnode.cwl
|
https://github.com/ncbi/pgap.git
Path: task_types/tt_hmmsearch_wnode.cwl Branch/Commit ID: 001e133e0eedaf0dd8447e3f8b3cc898ec6e3e1d |
|
|
|
HS Metrics workflow
|
https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/hs_metrics.cwl Branch/Commit ID: ffd73951157c61c1581d346628d75b61cdd04141 |
|
|
|
blastp_wnode_naming
|
https://github.com/ncbi/pgap.git
Path: task_types/tt_blastp_wnode_naming.cwl Branch/Commit ID: 3384fa5776c183d33bef830696b6edc6ec55a292 |
|
|
|
umi molecular alignment workflow
|
https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/molecular_qc.cwl Branch/Commit ID: 06d2440d115b446c299b4ce96e8812d2f8df86ec |
|
|
|
Motif Finding with HOMER with target and background regions from peaks
Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- 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-peak.cwl Branch/Commit ID: 799575ce58746813f066a665adeacdda252d8cab |
|
|
|
kmer_ref_compare_wnode
|
https://github.com/ncbi-gpipe/pgap.git
Path: task_types/tt_kmer_ref_compare_wnode.cwl Branch/Commit ID: 33414c888997d558bdcb558ca33c3a728a3e6143 |
|
|
|
count-lines4-wf.cwl
|
https://github.com/common-workflow-language/common-workflow-language.git
Path: v1.0/v1.0/count-lines4-wf.cwl Branch/Commit ID: e67f19d8a713759d761ecad050966d1eb043b85c |
|
|
|
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: 89ccbfc53ff3bb6abe2eb90bb7e0091c54c18f5c |
