Explore Workflows

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

Path: workflows/allele-process-strain.cwl

Branch/Commit ID: 4b8bb1a1ec39056253ca8eee976078e51f4a954e

This workflow detect and remove foreign chromosome from a DNA fasta file

https://github.com/ncbi/cwl-ngs-workflows-cbb.git

Path: workflows/Contamination/contamination-foreign-chromosome-blastn.cwl

Branch/Commit ID: 3247592a89deafaa0d9c5910a1cb1d000ef9b098

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

Path: tests/conditionals/cond-with-defaults.cwl

Branch/Commit ID: e62f99dd79d6cb9c157cceb458f74200da84f6e9

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

Path: testdata/step_valuefrom5_wf_v1_2.cwl

Branch/Commit ID: 0ad6983898f0d9001fe0f416f97c4d8b940e384a

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

Path: definitions/pipelines/immuno.cwl

Branch/Commit ID: 0805e8e0d358136468e0a9f49e06005e41965adc

https://github.com/mskcc/argos-cwl.git

Path: workflows/pair-workflow.cwl

Branch/Commit ID: 507efdf727d2a5ec7b91007e7c953b1a2d81b288

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

Path: tests/basename-fields-test.cwl

Branch/Commit ID: e62f99dd79d6cb9c157cceb458f74200da84f6e9

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

Path: tests/timelimit4-wf.cwl

Branch/Commit ID: e62f99dd79d6cb9c157cceb458f74200da84f6e9

Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy.

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

Path: workflows/pca.cwl

Branch/Commit ID: 2cad55523d1b4ee7fd9e64df0f6263c6545e4b0e

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: 4f48ee6f8665a34cdf96e89c012ee807f80c7a3d