Explore Workflows

View already parsed workflows here or click here to add your own

Graph Name Retrieved From View
workflow graph umi duplex alignment workflow

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

Path: definitions/subworkflows/duplex_alignment.cwl

Branch/Commit ID: b9e7392e72506cadd898a6ac4db330baf6535ab6
workflow graph rnaseq-alignment-quantification

This workflow retrieve SRA fastqc data and execute QC, alignment and quantification from TPMCalculator

 https://github.com/ncbi/cwl-ngs-workflows-cbb.git

Path: workflows/RNA-Seq/rnaseq-alignment-quantification.cwl

Branch/Commit ID: 793e327acc1d159ff601043ee88651fca62350dd
workflow graph ChIP-seq peak caller workflow MACS2 based

This workflow execute peak caller and QC for ChIP-seq using MACS2

 https://github.com/ncbi/cwl-ngs-workflows-cbb.git

Path: workflows/ChIP-Seq/peak-calling-MACS2.cwl

Branch/Commit ID: 793e327acc1d159ff601043ee88651fca62350dd
workflow graph adapter for sequence_align_and_tag

Some workflow engines won't stage files in our nested structure, so parse it out here

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

Path: definitions/subworkflows/sequence_align_and_tag_adapter.cwl

Branch/Commit ID: da335d9963418f7bedd84cb2791a0df1b3165ffe
workflow graph Motif Finding with HOMER with random background regions

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: 8049a781ac4aae579fbd3036fa0bf654532f15be
workflow graph taxonomy_check_16S

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 2d54b11cc9891c9aa52515fe4f8cd9cba12c6629
workflow graph Varscan Workflow

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

Path: definitions/subworkflows/varscan_pre_and_post_processing.cwl

Branch/Commit ID: 336f7d1af649f42543baa6be2594cd872919b5b5
workflow graph Detect Docm variants

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

Path: definitions/subworkflows/docm_cle.cwl

Branch/Commit ID: 9cbf2a483e1b9e4cdb8e2564be27a9e64fc1169e
workflow graph cache_asnb_entries

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

Path: task_types/tt_cache_asnb_entries.cwl

Branch/Commit ID: 5463361069e263ad6455858e054c1337b1d9e752
workflow graph process VCF workflow

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

Path: definitions/subworkflows/strelka_process_vcf.cwl

Branch/Commit ID: 7638b3075863ae8172f4adaec82fb2eb8e80d3d5