Explore Workflows

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

Path: cwltool/schemas/v1.0/v1.0/count-lines3-wf.cwl

Branch/Commit ID: 5ae5798f1c0c8d2178986b77cfd74edff510877a

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

Path: definitions/subworkflows/downsampled_alignment.cwl

Branch/Commit ID: da335d9963418f7bedd84cb2791a0df1b3165ffe

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: bf80c9339d81a78aefb8de661bff998ed86e836e

PGAP pipeline for external usage, powered via containers, simple user input: (FASTA + yaml only, no template)

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

Path: pgap.cwl

Branch/Commit ID: ea8c2aea64cab2241e02f4cb60ca9ca7593dfa58

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

Path: definitions/subworkflows/align_sort_markdup.cwl

Branch/Commit ID: da335d9963418f7bedd84cb2791a0df1b3165ffe

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

Path: definitions/subworkflows/varscan_germline.cwl

Branch/Commit ID: 051074fce4afd9732ef34db9dd43d3a1d8e979d6

somatic_exome is designed to perform processing of mutant/wildtype H.sapiens exome sequencing data. It features BQSR corrected alignments, 4 caller variant detection, and vep style annotations. Structural variants are detected via manta and cnvkit. In addition QC metrics are run, including somalier concordance metrics. example input file = analysis_workflows/example_data/somatic_exome.yaml

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

Path: definitions/pipelines/somatic_exome.cwl

Branch/Commit ID: 42c66dd24ce5026d3f717214ddb18b7b4fae93cf

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: ea8c2aea64cab2241e02f4cb60ca9ca7593dfa58

Pairwise comparison

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: ea8c2aea64cab2241e02f4cb60ca9ca7593dfa58

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

Path: task_types/tt_kmer_build_tree.cwl

Branch/Commit ID: ea8c2aea64cab2241e02f4cb60ca9ca7593dfa58