Explore Workflows

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

Path: cwltool/schemas/v1.0/v1.0/count-lines8-wf.cwl

Branch/Commit ID: fec7a10466a26e376b14181a88734983cfb1b8cb

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

Path: v1.0/v1.0/count-lines11-null-step-wf-noET.cwl

Branch/Commit ID: a5ae5ad0c9017ed625fb372f65e72dbb069439b0

Filters differentially expressed genes from DESeq for Tag Density Profile Analyses ================================================================================== Tool filters output from DESeq pipeline run for genes to create a file with regions of interest for Tag Density Profile Analyses.

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

Path: workflows/filter-deseq-for-heatmap.cwl

Branch/Commit ID: 1a46cb0e8f973481fe5ae3ae6188a41622c8532e

https://github.com/MG-RAST/pipeline.git

Path: CWL/Workflows/qc-assembled.workflow.cwl

Branch/Commit ID: 662d424d2e433e636f46a79025325d5daaca6271

run diamond on mutlple DBs and merge-sort results

https://github.com/MG-RAST/pipeline.git

Path: CWL/Workflows/protein-diamond.workflow.cwl

Branch/Commit ID: 81feefc84ec0faecf1ade718001d5f07610e616e

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: e0a30aa1ad516dd2ec0e9ce006428964b840daf4

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

Path: task_types/tt_hmmsearch_wnode.cwl

Branch/Commit ID: 22ffe27d9d4a899def7592d75d5871c1856adbdb

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

Path: progs/protein_extract.cwl

Branch/Commit ID: 22ffe27d9d4a899def7592d75d5871c1856adbdb

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

Path: definitions/subworkflows/hs_metrics.cwl

Branch/Commit ID: 0d2f354af9192a56af258a7d2426c7c160f4ec1a

### Devel version of QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data

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

Path: workflows/trim-quantseq-mrnaseq-se-strand-specific.cwl

Branch/Commit ID: 10ce6e113f749c7bd725e426445220c3bdc5ddf1