Explore Workflows

https://github.com/PMCC-BioinformaticsCore/janis-pipelines.git

Path: janis_pipelines/wgs_somatic/cwl/tools/BwaAligner_1_0_0.cwl

Branch/Commit ID: ccca639fe0b3a8104ff9fcfa285f1134706032b8

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

Path: definitions/subworkflows/filter_vcf_mouse.cwl

Branch/Commit ID: 0805e8e0d358136468e0a9f49e06005e41965adc

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

Path: definitions/subworkflows/single_sample_sv_callers.cwl

Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d

https://github.com/mskcc/pluto-cwl.git

Path: cwl/portal-workflow.cwl

Branch/Commit ID: 45604eaeea15030c7302941c761464ce392abf74

Workflow for running the facets suite workflow on a single tumor normal pair Includes handling of errors in case execution fails for the sample pair

https://github.com/mskcc/pluto-cwl.git

Path: cwl/facets-suite-workflow.cwl

Branch/Commit ID: 45604eaeea15030c7302941c761464ce392abf74

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: c9e7f3de7f6ba38ee663bd3f9649e8d7dbac0c86

Motif Finding with HOMER with custom 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. 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-bg.cwl

Branch/Commit ID: 60854b5d299df91e135e05d02f4be61f6a310fbc

Workflow for running Facets-suite on a set of tumor normal pairs This workflow scatters over all the pairs in the input JSON to run all samples in parallel Input JSON format ----------------- { \"pairs\": [ { \"tumor_bam\": { \"class\": \"File\", \"path\": \"/test_data/bam/Tumor1.rg.md.abra.printreads.bam\" }, \"normal_bam\": { \"class\": \"File\", \"path\": \"/test_data/bam/Normal1.rg.md.abra.printreads.bam\" }, \"pair_maf\": { \"class\": \"File\", \"path\": \"/test_data/bam/Tumor1.Normal1.maf\" }, \"pair_id\": \"Tumor1.Normal1\" }, { \"tumor_bam\": { \"class\": \"File\", \"path\": \"/test_data/bam/Tumor2.rg.md.abra.printreads.bam\" }, \"normal_bam\": { \"class\": \"File\", \"path\": \"/test_data/bam/Normal2.rg.md.abra.printreads.bam\" }, \"pair_maf\": { \"class\": \"File\", \"path\": \"/test_data/bam/Tumor2.Normal2.maf\" }, \"pair_id\": \"Tumor2.Normal2\" } ] } Output format ------------- output └── facets-suite ├── Tumor1.Normal1.arm_level.txt ├── Tumor1.Normal1.gene_level.txt ├── Tumor1.Normal1_hisens.ccf.maf ├── Tumor1.Normal1_hisens.rds ├── Tumor1.Normal1_hisens.seg ├── Tumor1.Normal1_purity.rds ├── Tumor1.Normal1_purity.seg ├── Tumor1.Normal1.qc.txt ├── Tumor1.Normal1.snp_pileup.gz ├── Tumor1.Normal1.txt ├── Tumor2.Normal2.arm_level.txt ├── Tumor2.Normal2.gene_level.txt ├── Tumor2.Normal2_hisens.ccf.maf ├── Tumor2.Normal2_hisens.rds ├── Tumor2.Normal2_hisens.seg ├── Tumor2.Normal2_purity.rds ├── Tumor2.Normal2_purity.seg ├── Tumor2.Normal2.qc.txt ├── Tumor2.Normal2.snp_pileup.gz ├── Tumor2.Normal2.txt └── logs ├── success └── failed

https://github.com/mskcc/pluto-cwl.git

Path: cwl/facets-workflow.cwl

Branch/Commit ID: 5cad957fec135aa55ca8d588372db0557ca1cad5

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

Path: definitions/pipelines/tumor_only_detect_variants.cwl

Branch/Commit ID: 788bdc99c1d5b6ee7c431c3c011eb30d385c1370

SoupX Estimate ==============

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

Path: workflows/soupx.cwl

Branch/Commit ID: c9e7f3de7f6ba38ee663bd3f9649e8d7dbac0c86