Explore Workflows

Single-cell Multiome ATAC and RNA-Seq Filtering Analysis Filters single-cell multiome ATAC and RNA-Seq datasets based on the common QC metrics.

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

Path: workflows/sc-multiome-filter.cwl

Branch/Commit ID: 7030da528559c7106d156284e50ff0ecedab0c4e

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

Path: definitions/subworkflows/docm_germline.cwl

Branch/Commit ID: 6a55118f915e24d2ad008c93a02d9de5643f5511

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: 5561f7ee11dd74848680351411a19aa87b13d27b

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

Path: bacterial_noncoding/wf_bacterial_noncoding.cwl

Branch/Commit ID: 1e16653514fd5629a704516eb447043c9fd0a53b

CNV Manta calling

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

Path: structuralvariants/cwl/subworkflows/cnv_manta.cwl

Branch/Commit ID: 3f6a871f81f343cf81a345f73ff2eeac70804b8c

Workflow to merge a large number of maf files into a single consensus bed file

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

Path: cwl/consensus_bed.cwl

Branch/Commit ID: d8a8af9fdb69c0a4003680c1d3b96f35d5e48f0e

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

Path: cwltool/schemas/v1.0/v1.0/count-lines5-wf.cwl

Branch/Commit ID: 2ae8117360a3cd4909d9d3f2b35c30bfffb25d0a

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: a2d6cd4c53bf3501f6bd79edebb7ca30bba8456f

https://github.com/mskcc/innovation-pipeline.git

Path: workflows/subworkflows/vcf_concat.cwl

Branch/Commit ID: b0f226a9ac5152f3afe0d38c8cd54aa25b8b01cf

# Genelists heatmap - peak and expression data visualized together This visualization workflow takes as input 1 or more genelists derived from the DESeq and/or diffbind workflows along with user-selected samples and visualizes the ChIP/ATAC-Seq peak and/or RNA-Seq expression data visualized together in a single morpheus heatmap. ### __References__ - Morpheus, https://software.broadinstitute.org/morpheus

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

Path: workflows/genelists-deseq-diffbind.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908