Explore Workflows
View already parsed workflows here or click here to add your own
Graph | Name | Retrieved From | View |
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Unaligned BAM to BQSR
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![]() Path: definitions/subworkflows/bam_to_bqsr.cwl Branch/Commit ID: 844c10a4466ab39c02e5bfa7a210c195b8efa77a |
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THOR - differential peak calling of ChIP-seq signals with replicates
What is THOR? -------------- THOR is an HMM-based approach to detect and analyze differential peaks in two sets of ChIP-seq data from distinct biological conditions with replicates. THOR performs genomic signal processing, peak calling and p-value calculation in an integrated framework. For more information please refer to: ------------------------------------- Allhoff, M., Sere K., Freitas, J., Zenke, M., Costa, I.G. (2016), Differential Peak Calling of ChIP-seq Signals with Replicates with THOR, Nucleic Acids Research, epub gkw680. |
![]() Path: workflows/rgt-thor.cwl Branch/Commit ID: e0a30aa1ad516dd2ec0e9ce006428964b840daf4 |
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03-map-pe.cwl
ATAC-seq 03 mapping - reads: PE |
![]() Path: v1.0/ATAC-seq_pipeline/03-map-pe.cwl Branch/Commit ID: 8d02684ae0ff27e641f3704686e3bc8b1979b854 |
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kmer_ref_compare_wnode
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![]() Path: task_types/tt_kmer_ref_compare_wnode.cwl Branch/Commit ID: b12ec8c8e832151033b9e6c0a76a3c3df18d45da |
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Subworkflow that runs cnvkit in single sample mode and returns a vcf file
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![]() Path: definitions/subworkflows/cnvkit_single_sample.cwl Branch/Commit ID: 35e6b3ef71b4a2a9caba1dbd5dc424a8809bcc0a |
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mosdepth_to_coverage_QC.cwl
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![]() Path: cwl/workflows/mosdepth_to_coverage_QC.cwl Branch/Commit ID: 2d9d8ae9a789a4b037b65dd1958649023b3d35c8 |
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tt_kmer_top_n.cwl
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![]() Path: task_types/tt_kmer_top_n.cwl Branch/Commit ID: be5ae41801b19ebc69a2889d8fdb39e8e2359611 |
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exome alignment and tumor-only variant detection
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![]() Path: definitions/pipelines/tumor_only_exome.cwl Branch/Commit ID: 74647cc0f1abac4ee22950cfa89c44cf2ca3cffd |
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exomeseq-gatk4-03-organizedirectories.cwl
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![]() Path: subworkflows/exomeseq-gatk4-03-organizedirectories.cwl Branch/Commit ID: e82f3a71183048dd6700ec6725ee526ac1a95238 |
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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) |
![]() Path: workflows/super-enhancer.cwl Branch/Commit ID: e0a30aa1ad516dd2ec0e9ce006428964b840daf4 |