Explore Workflows

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/conditionals/cond-wf-003_nojs.cwl

Branch/Commit ID: 31ec48a8d81ef7c1b2c5e9c0a19e7623efe4a1e2

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

Path: definitions/subworkflows/varscan.cwl

Branch/Commit ID: 4a04ad33e311c5e647cef848b74034477cb3c47e

Genomic regions intersection and visualization ============================================== 1. Merges intervals within each of the filtered peaks files from ChIP/ATAC experiments 2. Overlaps merged intervals and assigns the nearest genes to them

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

Path: workflows/intervene.cwl

Branch/Commit ID: 7eef0294395d83ff0765fce61726a59d71126422

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

Path: definitions/pipelines/detect_variants_nonhuman.cwl

Branch/Commit ID: 5be54bf09092c53e6c7797a875f64a360d511d7f

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: 7eef0294395d83ff0765fce61726a59d71126422

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

Path: testdata/step_valuefrom5_wf_with_id_v1_0.cwl

Branch/Commit ID: 124a08ce3389eb49066c34a4163cbbed210a0355

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

Path: task_types/tt_kmer_top_n.cwl

Branch/Commit ID: cec32f5b60c1d048257e3c3daed6912d5d2a054e

Perform taxonomic identification tasks on an input genome

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

Path: ani.cwl

Branch/Commit ID: 497175e1851779c57253d71144860747430d52b1

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

Path: tests/wf/default-dir5.cwl

Branch/Commit ID: efd59864c24d97e6d0d1d273025d3ef9003fa44d

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

Path: tests/wf/packed_no_main.cwl

Branch/Commit ID: 6d8c2a41e2c524e8d746020cc91711ecc3418a23

Packed ID: collision