Explore Workflows

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

Path: definitions/subworkflows/fp_filter.cwl

Branch/Commit ID: 0a9a4ce83b49ed4e7eee5bcc09d83725136a36b0

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

Path: task_types/tt_kmer_cache_retrieve.cwl

Branch/Commit ID: 72c3091012f5c2dce38ad9213cda617d2c7a61ac

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/scatter-valuefrom-wf2.cwl

Branch/Commit ID: 57baec040c99d7edef8242ef51b5470b1c82d733

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

Path: definitions/subworkflows/docm_cle.cwl

Branch/Commit ID: a9133c999502acf94b433af8d39897e6c2cdf65f

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

Path: definitions/pipelines/somatic_wgs_nonhuman.cwl

Branch/Commit ID: ef7f3345b352319ec22dffba26c79df033b141f9

GAT: Genomic Association Tester ============================================== A common question in genomic analysis is whether two sets of genomic intervals overlap significantly. This question arises, for example, in the interpretation of ChIP-Seq or RNA-Seq data. The Genomic Association Tester (GAT) is a tool for computing the significance of overlap between multiple sets of genomic intervals. GAT estimates significance based on simulation. Gat implemements a sampling algorithm. Given a chromosome (workspace) and segments of interest, for example from a ChIP-Seq experiment, gat creates randomized version of the segments of interest falling into the workspace. These sampled segments are then compared to existing genomic annotations. The sampling method is conceptually simple. Randomized samples of the segments of interest are created in a two-step procedure. Firstly, a segment size is selected from to same size distribution as the original segments of interest. Secondly, a random position is assigned to the segment. The sampling stops when exactly the same number of nucleotides have been sampled. To improve the speed of sampling, segment overlap is not resolved until the very end of the sampling procedure. Conflicts are then resolved by randomly removing and re-sampling segments until a covering set has been achieved. Because the size of randomized segments is derived from the observed segment size distribution of the segments of interest, the actual segment sizes in the sampled segments are usually not exactly identical to the ones in the segments of interest. This is in contrast to a sampling method that permutes segment positions within the workspace.

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

Path: workflows/gat-run.cwl

Branch/Commit ID: 7ced5a5259dbd8b3fc64456beaeffd44f4a24081

https://github.com/NCI-GDC/aliquot-maf-cwl.git

Path: vcf-to-aliquot-maf/subworkflows/extract_single_optional_file.cwl

Branch/Commit ID: af9e756697f29b082790b65f129a6434fd5c4980

https://github.com/apaul7/cancer-genomics-workflow.git

Path: definitions/pipelines/bisulfite.cwl

Branch/Commit ID: bfcb5ffbea3d00a38cc03595d41e53ea976d599d

ChIP-seq 02 trimming - reads: PE

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

Path: v1.0/ChIP-seq_pipeline/02-trim-pe.cwl

Branch/Commit ID: a502ff01b0857f8067aa541effc46a4c8b10d90f

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: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf