Explore Workflows

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

Graph Name Retrieved From View
workflow graph main-pisces-ras.cwl

https://github.com/bcbio/bcbio_validation_workflows.git

Path: somatic-lowfreq/pisces-ras-workflow/main-pisces-ras.cwl

Branch/Commit ID: 6e9274ca22e75e3359b5ff74c6657f300138bb95
workflow graph scatter-wf4.cwl#main

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

Path: tests/wf/scatter-wf4.cwl

Branch/Commit ID: cd1ba3df3745fba4b635f05c67ebeaf3b8a9f4ec

Packed ID: main
workflow graph umi duplex alignment workflow

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

Path: definitions/subworkflows/duplex_alignment.cwl

Branch/Commit ID: aba52e94b6d7470132d3c092c26d67e29d615300
workflow graph PGAP Pipeline, simple user input, PGAPX-134

PGAP pipeline for external usage, powered via containers, simple user input: (FASTA + yaml only, no template)

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

Path: pgap.cwl

Branch/Commit ID: 96dbb414d287f4382e2d477fb1851aeaa5f14f2b
workflow graph protein_extract

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

Path: progs/protein_extract.cwl

Branch/Commit ID: 55b6ee46b0c9fb1c9949cd0888b388c6f11b73b1
workflow graph Exome QC workflow

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

Path: definitions/subworkflows/qc_exome.cwl

Branch/Commit ID: 4a04ad33e311c5e647cef848b74034477cb3c47e
workflow graph mut.cwl

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

Path: tests/wf/mut.cwl

Branch/Commit ID: a94d75178c24ce77b59403fb8276af9ad1998929
workflow graph cache_test_workflow.cwl

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

Path: tests/wf/cache_test_workflow.cwl

Branch/Commit ID: 9cda157cb4380e9d30dec29f0452c56d0c10d064
workflow graph status_postgres.cwl

https://github.com/NCI-GDC/gdc-dnaseq-cwl.git

Path: workflows/dnaseq/status_postgres.cwl

Branch/Commit ID: b110a23e2efaaadfd4feca4f9e130946d1c5418d
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: 1f03ff02ef829bdb9d582825bcd4ca239e84ca2e