Explore Workflows

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

Path: workflows/ABRA/abra_workflow.cwl

Branch/Commit ID: 476f3dcda929ee9eb67391bbc819573d75751b7c

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

Path: workflows/waltz/waltz-workflow.cwl

Branch/Commit ID: ea7e777f1f5fb6683ba0a77ca38670153500ac46

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

Path: workflows/waltz/waltz_workflow_all_bams.cwl

Branch/Commit ID: c37b07afbf6bf4666adfc381db3062c661663815

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

Path: workflows/waltz/waltz-workflow.cwl

Branch/Commit ID: c37b07afbf6bf4666adfc381db3062c661663815

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

Path: workflows/marianas/collapsed_fastq_to_bam.cwl

Branch/Commit ID: 067d5b291322de0a1aa2546d130a480513a24ed9

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

Path: blast-pipelines/simple_three_step.cwl

Branch/Commit ID: 881c04b0ffd908244f838ff24a530e381ddfa34a

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

Path: tests/scatter-valuefrom-wf1.cwl

Branch/Commit ID: 5f27e234b4ca88ed1280dedf9e3391a01de12912

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

This is a subworkflow of the main oxog_varbam_annotat_wf workflow - this is not meant to be run as a stand-alone workflow!

https://github.com/svonworl/OxoG-Dockstore-Tools.git

Path: annotator_sub_wf.cwl

Branch/Commit ID: 40bf56daf9edab82e0f964dbd6961ca1111cd35f

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/abstract_operations/subworkflows/samtools_view_sam2bam.cwl

Branch/Commit ID: de9cb009f8fe0c8d5a94db5c882cf21ddf372452