Explore Workflows

Workflow runs [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) to build indices for reference genome provided in a single FASTA file as fasta_file input and GTF annotation file from annotation_gtf_file input. Generated indices are saved in a folder with the name that corresponds to the input genome.

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

Path: workflows/star-index.cwl

Branch/Commit ID: 7ced5a5259dbd8b3fc64456beaeffd44f4a24081

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

Path: task_types/tt_univec_wnode.cwl

Branch/Commit ID: 49732e54e2fe2eafd2f82df3c482c73e642f6d64

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

Path: testdata/stdout-wf_v1_0.cwl

Branch/Commit ID: 0ad6983898f0d9001fe0f416f97c4d8b940e384a

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: 7eef0294395d83ff0765fce61726a59d71126422

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

Path: testdata/scatter-wf2_v1_1.cwl

Branch/Commit ID: 0ad6983898f0d9001fe0f416f97c4d8b940e384a

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

Path: cwltool/schemas/v1.0/v1.0/env-wf3.cwl

Branch/Commit ID: 4635090ef98247b1902b3c7a25c007d9db1cb883

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

Path: tests/record-in-secondaryFiles-missing-wf.cwl

Branch/Commit ID: 57baec040c99d7edef8242ef51b5470b1c82d733

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

Path: task_types/tt_ani_top_n.cwl

Branch/Commit ID: 55e85a6c1a3d3ae2b47e9ae5363132a12824b1d9

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

Path: cwltool/schemas/v1.0/v1.0/search.cwl

Branch/Commit ID: cd779a90a4336563dcf13795111f502372c6af83

Packed ID: main

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

Path: definitions/pipelines/somatic_exome_cle_gathered.cwl

Branch/Commit ID: 457e101e3fb87e7fd792357afce00ed8ccbfbcdb