Explore Workflows

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

Path: v1.0/v1.0/echo-wf-default.cwl

Branch/Commit ID: 17695244222b0301b37cb749fe4a8d89622cd1ad

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

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

Branch/Commit ID: 203797516329f7fb8aa5e763e6f9b331c63c3060

Wrapped in a workflow SoupX tool for easy access to Cell Ranger pipeline compressed outputs.

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

Path: tools/soupx-subworkflow.cwl

Branch/Commit ID: 30031ca5e69cec603c4733681de54dc7bffa20a3

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

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

Path: tests/wf/revsort_step_bad_schema.cwl

Branch/Commit ID: f94719e862f86cc88600caf3628faba6c0d05042

Cellranger Reanalyze ====================

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

Path: workflows/cellranger-reanalyze.cwl

Branch/Commit ID: 30031ca5e69cec603c4733681de54dc7bffa20a3

For a given Zenodo community, list file content of its downloadable *.zip files

https://github.com/stain/ro-index-paper.git

Path: code/data-gathering/workflows/zenodo-zip-content.cwl

Branch/Commit ID: c0afa9b82e29ee0dfa83e903784ee54479567920

ATAC-seq 01 QC - reads: PE

https://github.com/Duke-GCB/GGR-cwl.git

Path: v1.0/ATAC-seq_pipeline/01-qc-pe.cwl

Branch/Commit ID: 6e68bda2cb45e8dc8e4d067c4220d65acfa53065

Motif Finding with HOMER with custom background regions --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis-bg.cwl

Branch/Commit ID: 30031ca5e69cec603c4733681de54dc7bffa20a3

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

Path: cwltool/schemas/v1.0/v1.0/conflict-wf.cwl

Branch/Commit ID: 49cd284a8fc7884de763573075d3e1d6a4c1ffdd

Packed ID: collision

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: 30031ca5e69cec603c4733681de54dc7bffa20a3