Explore Workflows

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Graph Name Retrieved From View
workflow graph umi molecular alignment workflow

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

Path: definitions/subworkflows/molecular_alignment.cwl

Branch/Commit ID: a23f42ef49c10a588fd35a3afaad5de03e253533
workflow graph wgs alignment and tumor-only variant detection

https://github.com/apaul7/cancer-genomics-workflow.git

Path: definitions/pipelines/tumor_only_wgs.cwl

Branch/Commit ID: bfcb5ffbea3d00a38cc03595d41e53ea976d599d
workflow graph env-wf1.cwl

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

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

Branch/Commit ID: 3ed10d0ea7ac57550433a89a92bdbe756bdb0e40
workflow graph Unaligned bam to sorted, markduped bam

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

Path: definitions/subworkflows/align_sort_markdup.cwl

Branch/Commit ID: 3bebaf9b70331de9f4845e2223c55082f5a812fb
workflow graph mut2.cwl

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

Path: tests/wf/mut2.cwl

Branch/Commit ID: cd779a90a4336563dcf13795111f502372c6af83
workflow graph transform_pack.cwl#amplicon_metrics.cwl

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/transform_pack.cwl

Branch/Commit ID: 3cb464a3a5c39cc060cd23d9c60918bc9ffb169b

Packed ID: amplicon_metrics.cwl
workflow graph step-valuefrom-wf.cwl

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

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

Branch/Commit ID: 4700fbee9a5a3271eef8bc9ee595619d0720431b
workflow graph original.cwl#main_pipeline

Simulation steps pipeline

 https://github.com/ILIAD-ocean-twin/application_package.git

Path: workflow_in_workflow/original.cwl

Branch/Commit ID: 9a0db98839bbc655e12d49f56c61deecd77ff14c

Packed ID: main_pipeline
workflow graph Cell Ranger Count Gene Expression

Cell Ranger Count Gene Expression =================================

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

Path: workflows/single-cell-preprocess-cellranger.cwl

Branch/Commit ID: bf80c9339d81a78aefb8de661bff998ed86e836e
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: 29bf638904709cfbf10908adcd51ba4886ace94a