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Graph Name Retrieved From View
workflow graph Cell Ranger Aggregate

Cell Ranger Aggregate =====================

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

Path: workflows/cellranger-aggr.cwl

Branch/Commit ID: 2005c6b7f1bff6247d015ff6c116bd9ec97158bb
workflow graph scRNA-seq pipeline using Salmon and Alevin

https://github.com/hubmapconsortium/salmon-rnaseq.git

Path: pipeline.cwl

Branch/Commit ID: 4d762e649bddffd89f18ec1c58a3242fe6c7c1fe
workflow graph 03-map-se-blacklist-removal.cwl

ATAC-seq 03 mapping - reads: SE - blacklist removal

 https://github.com/Duke-GCB/GGR-cwl.git

Path: v1.0/ATAC-seq_pipeline/03-map-se-blacklist-removal.cwl

Branch/Commit ID: 67e8ccd5abddbd9e27f23ceeb95536fecf792d93
workflow graph Run pindel on provided region

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

Path: definitions/subworkflows/pindel_region.cwl

Branch/Commit ID: c23dc7f113ca0b0a3127a5d6c696e98d4799460c
workflow graph Build Bismark indices

Copy fasta_file file to the folder and run run bismark_genome_preparation script to prepare indices for Bismark Methylation Analysis. Bowtie2 aligner is used by default. The name of the output indices folder is equal to the genome input.

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

Path: workflows/bismark-index.cwl

Branch/Commit ID: 46a077b51619c6a14f85e0aa5260ae8a04426fab
workflow graph tt_fscr_calls_pass1

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

Path: task_types/tt_fscr_calls_pass1.cwl

Branch/Commit ID: 1b9094d70f620bb2e51072dd2150150aa4927439
workflow graph 05-quantification.cwl

ATAC-seq - Quantification

 https://github.com/Duke-GCB/GGR-cwl.git

Path: v1.0/ATAC-seq_pipeline/05-quantification.cwl

Branch/Commit ID: 8aabde14169421a7115c5cd48c4740b3a7bd818f
workflow graph main-NA24385-sv.cwl

https://github.com/bcbio/bcbio_validation_workflows.git

Path: NA24385-sv/NA24385-sv-workflow/main-NA24385-sv.cwl

Branch/Commit ID: af9a5621efcb44c249697d6df071fe4defe389ac
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: 799575ce58746813f066a665adeacdda252d8cab
workflow graph gcaccess_from_list

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

Path: task_types/tt_gcaccess_from_list.cwl

Branch/Commit ID: a402541b8530f30eab726c160da90a23036847a1