Explore Workflows

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

Path: task_types/tt_blastn_wnode.cwl

Branch/Commit ID: c6e7e18969c761803c38762ad6ee91b0001c52e2

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

Path: task_types/tt_univec_wnode.cwl

Branch/Commit ID: 61e3752f1f5e2ee498fa024c235226f8580be942

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

Path: tests/count-lines1-wf.cwl

Branch/Commit ID: c7c97715b400ff2194aa29fc211d3401cea3a9bf

Pairwise comparison

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: 33dcc054a8718edad26440f085d73b7c5d7b7871

Feature expression merge - combines feature expression from several experiments ========================================================================= Workflows merges RPKM (by default) gene expression from several experiments based on the values from GeneId, Chrom, TxStart, TxEnd and Strand columns (by default). Reported unique columns are renamed based on the experiments names.

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

Path: workflows/feature-merge.cwl

Branch/Commit ID: b4d578c2ba4713a5a22163d9f8c7105acda1f22e

Whole Exome Sequence analysis GATK4 Preprocessing

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

Path: workflows/exomeseq-gatk4.cwl

Branch/Commit ID: 66b46c15d266fdf6a1faabd8d2f1b257f3438efc

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