Explore Workflows

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

Graph Name Retrieved From View
workflow graph collapsed_fastq_to_bam.cwl

https://github.com/mskcc/Innovation-Pipeline.git

Path: workflows/marianas/collapsed_fastq_to_bam.cwl

Branch/Commit ID: 476f3dcda929ee9eb67391bbc819573d75751b7c
workflow graph qc_workflow_wo_waltz.cwl

This workflow is intended to be used to test the QC module, without having to run the long waltz step

https://github.com/mskcc/Innovation-Pipeline.git

Path: workflows/QC/qc_workflow_wo_waltz.cwl

Branch/Commit ID: 476f3dcda929ee9eb67391bbc819573d75751b7c
workflow graph abra_workflow.cwl

https://github.com/mskcc/Innovation-Pipeline.git

Path: workflows/ABRA/abra_workflow.cwl

Branch/Commit ID: 476f3dcda929ee9eb67391bbc819573d75751b7c
workflow graph waltz-workflow.cwl

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/waltz/waltz-workflow.cwl

Branch/Commit ID: ea7e777f1f5fb6683ba0a77ca38670153500ac46
workflow graph waltz_workflow_all_bams.cwl

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/waltz/waltz_workflow_all_bams.cwl

Branch/Commit ID: c37b07afbf6bf4666adfc381db3062c661663815
workflow graph waltz-workflow.cwl

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/waltz/waltz-workflow.cwl

Branch/Commit ID: c37b07afbf6bf4666adfc381db3062c661663815
workflow graph collapsed_fastq_to_bam.cwl

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/marianas/collapsed_fastq_to_bam.cwl

Branch/Commit ID: 067d5b291322de0a1aa2546d130a480513a24ed9
workflow graph BLASTP, parse, dump FASTA

https://github.com/ncbi/cwl-demos.git

Path: blast-pipelines/simple_three_step.cwl

Branch/Commit ID: 881c04b0ffd908244f838ff24a530e381ddfa34a
workflow graph scatter-valuefrom-wf1.cwl

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/scatter-valuefrom-wf1.cwl

Branch/Commit ID: 5f27e234b4ca88ed1280dedf9e3391a01de12912
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: bfa3843bcf36125ff258d6314f64b41336f06e6b