Explore Workflows

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

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: bba6c580ab88e077f6aa2c2ee7c73159f3f9156e

https://github.com/PMCC-BioinformaticsCore/janis-pipelines.git

Path: janis_pipelines/wgs_somatic/cwl/tools/mergeAndMarkBams_4_1_3.cwl

Branch/Commit ID: ccca639fe0b3a8104ff9fcfa285f1134706032b8

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

Path: definitions/subworkflows/phase_vcf.cwl

Branch/Commit ID: ae75b938e6e8ae777a55686bbacad824b3c6788c

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: 1a46cb0e8f973481fe5ae3ae6188a41622c8532e

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

Path: tests/count-lines1-wf.cwl

Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5

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

Path: tests/wf/mut.cwl

Branch/Commit ID: cb81b22abc52838823da9945f04d06739ab32fda

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

Path: tests/wf/conflict.cwl

Branch/Commit ID: 981c03099f79b5aad74555787d406f695dd0b320

Packed ID: main

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

Path: task_types/tt_cache_asnb_entries.cwl

Branch/Commit ID: 1bf7dc7b03ea3c64e54375cc5c3767849a801000

FastQC - a quality control tool for high throughput sequence data ===================================== FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis. The main functions of FastQC are: - Import of data from FastQ files (any variant) - Providing a quick overview to tell you in which areas there may be problems - Summary graphs and tables to quickly assess your data - Export of results to an HTML based permanent report - Offline operation to allow automated generation of reports without running the interactive application

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

Path: workflows/fastqc.cwl

Branch/Commit ID: 4a5c59829ff8b9f3c843e66e3c675dcd9c689ed5

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: 7f857f7f2d7c080d27c775b67a6d6f7d94bce31f