Explore Workflows

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Graph Name Retrieved From View
workflow graph dynresreq-workflow-tooldefault.cwl

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

Path: v1.0/v1.0/dynresreq-workflow-tooldefault.cwl

Branch/Commit ID: 9a23706ec061c5d2c02ff60238d218aadf0b5db9
workflow graph RNA-Seq pipeline single-read stranded mitochondrial

Slightly changed 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. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with single-read strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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-mitochondrial.cwl

Branch/Commit ID: ddc35c559d1ac6aab4972fe1a2b63300c4373f54
workflow graph step-valuefrom3-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/step-valuefrom3-wf.cwl

Branch/Commit ID: e8b3565a008d95859fc44227987a54e6a53a8c29
workflow graph Bisulfite QC tools

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

Path: definitions/subworkflows/bisulfite_qc.cwl

Branch/Commit ID: 3f3b186da9bf82a5e2ae74ba27aef35a46174ebe
workflow graph step-valuefrom-wf.cwl

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

Path: tests/step-valuefrom-wf.cwl

Branch/Commit ID: e515226f8ac0f7985cd94dae4a301150adae3050
workflow graph Whole genome alignment and somatic variant detection

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

Path: definitions/pipelines/somatic_wgs.cwl

Branch/Commit ID: 3034168d652bfa930ba09af20e473a4564a8010d
workflow graph THOR - differential peak calling of ChIP-seq signals with replicates

What is THOR? -------------- THOR is an HMM-based approach to detect and analyze differential peaks in two sets of ChIP-seq data from distinct biological conditions with replicates. THOR performs genomic signal processing, peak calling and p-value calculation in an integrated framework. For more information please refer to: ------------------------------------- Allhoff, M., Sere K., Freitas, J., Zenke, M., Costa, I.G. (2016), Differential Peak Calling of ChIP-seq Signals with Replicates with THOR, Nucleic Acids Research, epub gkw680.

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

Path: workflows/rgt-thor.cwl

Branch/Commit ID: 87f213456b3f966b773d396cce1fe5a272dad858
workflow graph tt_kmer_top_n.cwl

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

Path: task_types/tt_kmer_top_n.cwl

Branch/Commit ID: 7b21dc40840852f3942c31b9c472346ea3f9a3ca
workflow graph count-lines9-wf-noET.cwl

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

Path: v1.0/v1.0/count-lines9-wf-noET.cwl

Branch/Commit ID: 9a23706ec061c5d2c02ff60238d218aadf0b5db9
workflow graph Motif Finding with HOMER with target and background regions from peaks

Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- 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-peak.cwl

Branch/Commit ID: 7ae3b75bbe614e59cdeaba06047234a6c40c0fe9