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Graph Name Retrieved From View
workflow graph Single-Cell Differential Abundance Analysis

Single-Cell Differential Abundance Analysis Compares the composition of cell types between two tested conditions

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

Path: workflows/sc-rna-da-cells.cwl

Branch/Commit ID: b4d578c2ba4713a5a22163d9f8c7105acda1f22e
workflow graph varscan somatic workflow

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

Path: definitions/subworkflows/varscan.cwl

Branch/Commit ID: 86fbeb95ef85111f3b4c6bc2bba8f06cef64e157
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: 7eef0294395d83ff0765fce61726a59d71126422
workflow graph scatterfail.cwl

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

Path: tests/wf/scatterfail.cwl

Branch/Commit ID: 227f35a5ed50c423afba2353871950aa61d58872
workflow graph bgzip and index VCF

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

Path: definitions/subworkflows/bgzip_and_index.cwl

Branch/Commit ID: 86fbeb95ef85111f3b4c6bc2bba8f06cef64e157
workflow graph assm_assm_blastn_wnode

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

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: 9e43bc5cff985574e1f8135d4c50b5a347517c9e
workflow graph wgs alignment and germline variant detection

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

Path: definitions/pipelines/germline_wgs.cwl

Branch/Commit ID: a670f323e77e02d9b77be9a13d73d5276dd3676c
workflow graph trim-chipseq-se.cwl

Runs ChIP-Seq BioWardrobe basic analysis with single-end data file.

https://github.com/Barski-lab/workflows.git

Path: workflows/trim-chipseq-se.cwl

Branch/Commit ID: b4b7b2e7e508be5eac639f9e323d141daf714c0d
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: 4f48ee6f8665a34cdf96e89c012ee807f80c7a3d
workflow graph bulk scRNA-seq pipeline using Salmon

https://github.com/hubmapconsortium/salmon-rnaseq.git

Path: bulk-pipeline.cwl

Branch/Commit ID: 2dc339d7d922610af97fde5d6d25798d0bdd5441