Explore Workflows

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

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

Branch/Commit ID: 4e4d2e674bde612f98f2b0370445f8b2a47587df

Interval overlapping alignments counts ====================================== Reports the count of alignments from multiple samples that overlap specific intervals.

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

Path: workflows/bedtools-multicov.cwl

Branch/Commit ID: 10ce6e113f749c7bd725e426445220c3bdc5ddf1

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

Path: cwltool/schemas/v1.0/v1.0/any-type-compat.cwl

Branch/Commit ID: fec7a10466a26e376b14181a88734983cfb1b8cb

https://github.com/mskcc/roslin-variant.git

Path: setup/cwl/conpair/0.2/conpair-master.cwl

Branch/Commit ID: 72353a747f45ffe35863c3b6c70b28a676a97643

AltAnalyze Build Reference Indices ==================================

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

Path: workflows/altanalyze-prepare-genome.cwl

Branch/Commit ID: 581156366f91861bd4dbb5bcb59f67d468b32af3

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

Path: workflows/bamfastq_align/extract_amplicon_kit_http.cwl

Branch/Commit ID: 2f7c0e92e7b88b57b86602f11c595661c374008a

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: 8bf36bfad5624fbc8fc315e82783a44e9e5e4470

https://github.com/common-workflow-lab/2020-covid-19-bh.git

Path: msa/msa.cwl

Branch/Commit ID: 8fd2d9814a5641a55efd8e63fa65a652b66f9d0b

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

Path: cwltool/schemas/v1.0/v1.0/count-lines10-wf.cwl

Branch/Commit ID: 0db44e3c9805a070564f954222efff71cd791b70

Reverse the lines in a document, then sort those lines.

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

Path: tests/wf/revsort.cwl

Branch/Commit ID: 0e8110083bad6ea98fc487aa262953a6c5e010b5