Explore Workflows

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

Graph Name Retrieved From View
workflow graph indices-header.cwl

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

Path: metadata/indices-header.cwl

Branch/Commit ID: dda6e8b5ada3f106a2b3bfcc1b151eccf9977726
workflow graph macs2.cwl

string

 https://github.com/pitagora-network/DAT2-cwl.git

Path: workflow/epigenome-chip-seq/macs2/macs2.cwl

Branch/Commit ID: 0cd20e1be620ae0817a1aa4286d73b78c89809f0
workflow graph epigenome-chip-seq.cwl

https://github.com/pitagora-network/DAT2-cwl.git

Path: workflow/epigenome-chip-seq/epigenome-chip-seq.cwl

Branch/Commit ID: 0cd20e1be620ae0817a1aa4286d73b78c89809f0
workflow graph DESeq - differential gene expression analysis

Differential gene expression analysis ===================================== Differential gene expression analysis based on the negative binomial distribution Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. DESeq1 ------ High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. Simon Anders and Wolfgang Huber propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq.html), as an R/Bioconductor package DESeq2 ------ In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. [DESeq2](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

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

Path: workflows/deseq.cwl

Branch/Commit ID: 7ced5a5259dbd8b3fc64456beaeffd44f4a24081
workflow graph RNA-Seq alignment and transcript/gene abundance workflow

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

Path: definitions/pipelines/rnaseq.cwl

Branch/Commit ID: 449bc7e45bb02316d040f73838ef18359e770268
workflow graph Subworkflow to allow calling cnvkit with cram instead of bam files

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

Path: definitions/subworkflows/cram_to_cnvkit.cwl

Branch/Commit ID: e0b3c76e38630fb6234414b5adebfb6a4fb23117
workflow graph Generate ATDP heatmap using Homer

Generate ATDP heatmap centered on TSS from an array of input BAM files and genelist TSV file. Returns array of heatmap JSON files with the names that have the same basenames as input BAM files, but with .json extension

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

Path: workflows/heatmap.cwl

Branch/Commit ID: 3fc68366adb179927af5528c27b153abaf94494d
workflow graph map-ordering-v1_0.cwl

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

Path: testdata/map-ordering-v1_0.cwl

Branch/Commit ID: 0ab1d42d10f7311bb4032956c4a6f3d2730d9507
workflow graph Bisulfite alignment and QC

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

Path: definitions/pipelines/bisulfite.cwl

Branch/Commit ID: d3e4bf55753cd92f97537c7d701187ea92d1e5f0
workflow graph xenbase-chipseq-pe.cwl

XenBase workflow for analysing ChIP-Seq paired-end data

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

Path: workflows/xenbase-chipseq-pe.cwl

Branch/Commit ID: ca2dbb71d0537b1d93a8bd44719250cf8949b157