Explore Workflows

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

Path: definitions/subworkflows/germline_detect_variants.cwl

Branch/Commit ID: ad65dc1dfff9afa5077f498b85e699716c47f6cb

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/count-lines2-wf.cwl

Branch/Commit ID: 707ebcd2173889604459c5f4ffb55173c508abb3

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/count-lines11-wf.cwl

Branch/Commit ID: a0f2d38e37ff51721fdeaf993bb2ab474b17246b

Creates indices for: * [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) * [bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) It performs the following steps: 1. `STAR --runMode genomeGenerate` to generate indices, based on [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) and [GTF](http://mblab.wustl.edu/GTF2.html) input files, returns results as an array of files 2. Outputs indices as [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates *chrNameLength.txt* file from Directory output 4. `bowtie-build` to generate indices requires genome [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) file as input, returns results as a group of main and secondary files

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

Path: workflows/genome-indices.cwl

Branch/Commit ID: e99e80a2c19682d59947bde04a892d7b6d90091c

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

Path: definitions/pipelines/detect_variants_wgs.cwl

Branch/Commit ID: 27dcb1ae121be6a23057b74332b8c752ea425735

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/conditionals/cond-wf-003_nojs.cwl

Branch/Commit ID: 57baec040c99d7edef8242ef51b5470b1c82d733

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: e99e80a2c19682d59947bde04a892d7b6d90091c

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

Path: cwltool/schemas/v1.0/v1.0/env-wf2.cwl

Branch/Commit ID: 047e69bb169e79fad6a7285ee798c4ecec3b218b

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: 49732e54e2fe2eafd2f82df3c482c73e642f6d64

https://git.astron.nl/RD/LINC.git

Path: workflows/LBA_target.cwl

Branch/Commit ID: ee2e8e751a5202b670d6543d932757c00fb3bb03