Explore Workflows

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

Graph Name Retrieved From View
workflow graph DiffBind - Differential Binding Analysis of ChIP-Seq Peak Data

Differential Binding Analysis of ChIP-Seq Peak Data --------------------------------------------------- DiffBind processes ChIP-Seq data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by ChIP-Seq peak callers and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4.

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

Path: workflows/diffbind.cwl

Branch/Commit ID: 282762f8bbaea57dd488115745ef798e128bade1
workflow graph scatter-wf1.cwl

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

Path: cwltool/schemas/v1.0/v1.0/scatter-wf1.cwl

Branch/Commit ID: c6cced7a2e6389d2eb43342e702677ccb7c7497c
workflow graph Cell Ranger Reference (VDJ)

Cell Ranger Reference (VDJ) Builds a reference genome of a selected species for V(D)J contigs assembly and clonotype calling. The results of this workflow are used in “Cell Ranger Count (RNA+VDJ)” pipeline.

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

Path: workflows/cellranger-mkvdjref.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869
workflow graph chipseq-gen-bigwig.cwl

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

Path: subworkflows/chipseq-gen-bigwig.cwl

Branch/Commit ID: d6ec0dee61ef65a110e10141bde1a79332a64ab0
workflow graph Index building sub-workflow

Build genome and transcriptome indexes for alignment, quantification.

https://github.com/Sage-Bionetworks/amp-workflows.git

Path: amp-rnaseq_reprocessing/amp-rnaseq_reprocess-workflow/wf-buildindexes.cwl

Branch/Commit ID: 50e640ce16be4287b2e87381850a211ad3e70dc1
workflow graph step-valuefrom2-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/step-valuefrom2-wf.cwl

Branch/Commit ID: b82ce7ae901a54c7a062fd5eefd8d5ceb5a4d684
workflow graph RNA-Seq pipeline paired-end strand specific

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **paired-end** experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the paired-end RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 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) 4. Generate BigWig file on the base of sorted BAM file 5. Map input FASTQ files to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 6. 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-pe-dutp.cwl

Branch/Commit ID: 2b8146f76595f0c4d8bf692de78b21280162b1d0
workflow graph ROSE: rank ordering of super-enhancers

Super-enhancers, consist of clusters of enhancers that are densely occupied by the master regulators and Mediator. Super-enhancers differ from typical enhancers in size, transcription factor density and content, ability to activate transcription, and sensitivity to perturbation. Use to create stitched enhancers, and to separate super-enhancers from typical enhancers using sequencing data (.bam) given a file of previously identified constituent enhancers (.gff)

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

Path: workflows/super-enhancer.cwl

Branch/Commit ID: ebbf23764ede324cabc064bd50647c1f643726fa
workflow graph gcaccess_from_list

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

Path: task_types/tt_gcaccess_from_list.cwl

Branch/Commit ID: 733ab7198a66a0153d0f03c3022ab53c17325ff8
workflow graph PCA - Principal Component Analysis

Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy.

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

Path: workflows/pca.cwl

Branch/Commit ID: ebbf23764ede324cabc064bd50647c1f643726fa