Explore Workflows

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

Graph Name Retrieved From View
workflow graph bulk_process.cwl

https://github.com/hubmapconsortium/sc-atac-seq-pipeline.git

Path: steps/bulk_process.cwl

Branch/Commit ID: e1af1eb62aa9f757bded9b995411d25e098b3572
workflow graph format_rrnas_from_seq_entry

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

Path: task_types/tt_format_rrnas_from_seq_entry.cwl

Branch/Commit ID: a402541b8530f30eab726c160da90a23036847a1
workflow graph wgs alignment and germline variant detection

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

Path: definitions/pipelines/germline_wgs_gvcf.cwl

Branch/Commit ID: 04d21c33a5f2950e86db285fa0a32a6659198d8a
workflow graph nestedworkflows.cwl

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

Path: _includes/cwl/22-nested-workflows/nestedworkflows.cwl

Branch/Commit ID: fb086088825d19c1136b97dd5997a060da8d44d6
workflow graph taxonomy_check_16S

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 1b9094d70f620bb2e51072dd2150150aa4927439
workflow graph ROSE: rank ordering of super-enhancers

Super-enhancers, consist of clusters of enhancers that are densely occupied by the master regulators and Mediator. Super-enhancers differ from typical enhancers in size, transcription factor density and content, ability to activate transcription, and sensitivity to perturbation. Use to create stitched enhancers, and to separate super-enhancers from typical enhancers using sequencing data (.bam) given a file of previously identified constituent enhancers (.gff)

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

Path: workflows/super-enhancer.cwl

Branch/Commit ID: 2005c6b7f1bff6247d015ff6c116bd9ec97158bb
workflow graph DiffBind - Differential Binding Analysis of ChIP-Seq Peak Data

Differential Binding Analysis of ChIP-Seq Peak Data --------------------------------------------------- DiffBind processes ChIP-Seq data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by ChIP-Seq peak callers and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4.

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

Path: workflows/diffbind.cwl

Branch/Commit ID: 799575ce58746813f066a665adeacdda252d8cab
workflow graph strelka workflow

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

Path: definitions/subworkflows/strelka_and_post_processing.cwl

Branch/Commit ID: 336f7d1af649f42543baa6be2594cd872919b5b5
workflow graph CLE gold vcf evaluation workflow

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

Path: definitions/subworkflows/vcf_eval_cle_gold.cwl

Branch/Commit ID: c23dc7f113ca0b0a3127a5d6c696e98d4799460c
workflow graph PCA - Principal Component Analysis

Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy.

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

Path: workflows/pca.cwl

Branch/Commit ID: 46a077b51619c6a14f85e0aa5260ae8a04426fab