Explore Workflows

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

Graph Name Retrieved From View
workflow graph gathered exome alignment and somatic variant detection

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

Path: definitions/pipelines/somatic_exome_gathered.cwl

Branch/Commit ID: 8da2b1cd6fa379b2c22baf9dad762d39630e6f46
workflow graph workflow.cwl

https://github.com/Marco-Salvi/cwl-test.git

Path: workflow.cwl

Branch/Commit ID: 5cf11efca9e44b1ae6037a4ef768987808cfd4f9
workflow graph iwdr-passthrough-successive.cwl

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

Path: tests/wf/iwdr-passthrough-successive.cwl

Branch/Commit ID: 55ccde7c2fe3e7899136ce8606a341e292d7050a
workflow graph scatter-wf3.cwl#main

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

Path: cwltool/schemas/v1.0/v1.0/scatter-wf3.cwl

Branch/Commit ID: 280a852e74aec08cf79687e8004e17b1ab464534

Packed ID: main
workflow graph count-lines12-wf.cwl

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/count-lines12-wf.cwl

Branch/Commit ID: 3e90671b25f7840ef2926ad2bacbf447772dda94
workflow graph umi molecular alignment workflow

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

Path: definitions/subworkflows/molecular_qc.cwl

Branch/Commit ID: d297528e53b6c1ecb69b1ab27b8e03323b4463ad
workflow graph predictions.cwl

https://github.com/simleo/workflow-run-crate.git

Path: docs/examples/draft/ml-predict-pipeline-streamflow/predictions.cwl

Branch/Commit ID: d94348eabb80ca766f845f6eded4ec74e3fdcef5
workflow graph Filter single sample sv vcf from depth callers(cnvkit/cnvnator)

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

Path: definitions/subworkflows/sv_depth_caller_filter.cwl

Branch/Commit ID: a28a8077a8c4dbf117d16799807483a2532af3f3
workflow graph RNA-Seq pipeline single-read strand specific

Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific single-read** experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 3. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 5. Generate BigWig file on the base of sorted BAM file 6. Map input FASTQ file to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 7. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; export results to file

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

Path: workflows/rnaseq-se-dutp.cwl

Branch/Commit ID: cbefc215d8286447620664fb47076ba5d81aa47f
workflow graph GAT - Genomic Association Tester

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: a1f6ca50fcb0881781b3ba0306dd61ebf555eaba