Explore Workflows

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

Path: bacterial_noncoding/wf_gcmsearch.cwl

Branch/Commit ID: 609aead9804a8f31fa9b3dbc7e52105aec487f31

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/bwa_mem.cwl

Branch/Commit ID: 7fe278136146cbe6567816f1819f0725afeba021

CNV Manta calling

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/cnv_manta.cwl

Branch/Commit ID: 7fe278136146cbe6567816f1819f0725afeba021

The \"main\" workflow. Takes fastq files generated using the seCLIP protocol (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5991800/) and outputs candidate RBP binding regions (peaks). runs: wf_get_peaks_se.cwl through scatter across multiple samples.

https://github.com/YeoLab/eclip.git

Path: cwl/wf_get_peaks_scatter_se.cwl

Branch/Commit ID: 49a9bcda10de8f55fab2481f424eb9cdf2e5b256

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: 9e3c3e65c19873cd1ed3cf7cc3b94ebc75ae0cc5

Workflow for running the facets suite workflow on a single tumor normal pair Includes handling of errors in case execution fails for the sample pair

https://github.com/mskcc/pluto-cwl.git

Path: cwl/facets-suite-workflow.cwl

Branch/Commit ID: d8a8af9fdb69c0a4003680c1d3b96f35d5e48f0e

CNV ExomeDepth calling

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/cnv_exome_depth.cwl

Branch/Commit ID: 7fe278136146cbe6567816f1819f0725afeba021

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

Path: definitions/subworkflows/downsampled_alignment.cwl

Branch/Commit ID: f9600f9959acdc30259ba7e64de61104c9b01f0b

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

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: 609aead9804a8f31fa9b3dbc7e52105aec487f31

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

Path: task_types/tt_kmer_build_tree.cwl

Branch/Commit ID: d39017c63dd8e088f1ad3809d709529df602e05f