Explore Workflows

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

Path: definitions/subworkflows/cram_to_cnvkit.cwl

Branch/Commit ID: d2c2f2eb846ae2e9cdcab46e3bb88e42126cb3f5

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

Path: testdata/count-lines7-wf_v1_1.cwl

Branch/Commit ID: 77669d4dd1d1ebd2bdd9810f911608146d9b8e51

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: 550682d2fe3348161eab1b8612e48a59af4ac6a5

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: 550682d2fe3348161eab1b8612e48a59af4ac6a5

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

Path: tests/pass-unconnected.cwl

Branch/Commit ID: ea9f8634e41824ac3f81c3dde698d5f0eef54f1b

DNase-seq 02 quantification

https://github.com/alexbarrera/GGR-cwl.git

Path: v1.0/DNase-seq_pipeline/02-peakcall.cwl

Branch/Commit ID: 13826f526a99e151e9cf5f22e70bdcf4feea73f4

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/bwa_index.cwl

Branch/Commit ID: 637e294ff72687314faacef2c30cb46874611e50

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: 4f48ee6f8665a34cdf96e89c012ee807f80c7a3d

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: 2b8146f76595f0c4d8bf692de78b21280162b1d0

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

Path: protein_alignment/wf_seed.cwl

Branch/Commit ID: 22ffe27d9d4a899def7592d75d5871c1856adbdb