Explore Workflows

Simulation steps pipeline

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

Path: workflow_in_workflow/workflow_same_level_v2.cwl

Branch/Commit ID: aab1b560c6a855c645f43635f155a15800a7ce0d

Packed ID: main_pipeline

Simulation steps pipeline

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

Path: workflow_in_workflow/workflow_same_level.cwl

Branch/Commit ID: aab1b560c6a855c645f43635f155a15800a7ce0d

Packed ID: main_pipeline

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

Path: testdata/workflow_input_format_expr.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

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

Path: testdata/count-lines7-wf_v1_2.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

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

Path: testdata/workflow_input_sf_expr_array_v1_1.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

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

Path: testdata/workflow_input_sf_expr_v1_1.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

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

Path: testdata/step_valuefrom5_wf_v1_1.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

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

Path: testdata/workflow_input_sf_expr_array.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

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

Path: testdata/step-valuefrom2-wf_v1_1.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

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: ddc35c559d1ac6aab4972fe1a2b63300c4373f54