Explore Workflows

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

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

Path: workflows/cellranger-arc-aggr.cwl

Branch/Commit ID: 480e99a4bb3046e0565113d9dca294e0895d3b0c

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

Path: task_types/tt_ani_top_n.cwl

Branch/Commit ID: 17bae57a1f00f5c6db8f3a82d86262f12b8153cf

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

Path: definitions/subworkflows/varscan_germline.cwl

Branch/Commit ID: fbeea265295ae596d5a3ba563e766be0c4fc26e8

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

Path: definitions/subworkflows/varscan.cwl

Branch/Commit ID: 6949082038c1ad36d6e9848b97a2537aef2d3805

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

Path: definitions/pipelines/rnaseq_star_fusion.cwl

Branch/Commit ID: 788bdc99c1d5b6ee7c431c3c011eb30d385c1370

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: CreateSymlink-workflow.cwl

Branch/Commit ID: 0b58c250e8ab7c5efae29443f08ea74316127041

This workflow step takes the PeptideProphet output files from the first step containing the peptide validation and calculates the protein inference using ProteinProphet.

https://github.com/cwl-apps/fragpipe-proteomics-pipeline-tutorial.git

Path: FragPipe-ProteinProphet/fragpipe-proteinprophet.cwl

Branch/Commit ID: 080063b7443c98bc378935e16f747f8c542c9521

Pairwise comparison

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: 2801ce53744a085580a8de91cd007c45146b51e8

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

Path: definitions/subworkflows/duplex_alignment.cwl

Branch/Commit ID: 061d3a2fbcd8a1c39c0b38c549e528deb24a9d54

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: 480e99a4bb3046e0565113d9dca294e0895d3b0c