Explore Workflows

View already parsed workflows here or click here to add your own

Graph Name Retrieved From View
workflow graph DESeq - differential gene expression analysis

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: c6bfa0de917efb536dd385624fc7702e6748e61d
workflow graph count-lines16-wf.cwl

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/count-lines16-wf.cwl

Branch/Commit ID: 664835e83eb5e57eee18a04ce7b05fb9d70d77b7
workflow graph exome alignment and germline variant detection, with optitype for HLA typing

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

Path: definitions/pipelines/germline_exome_hla_typing.cwl

Branch/Commit ID: 8dc462a7d9ba1479f764682af99c69d8574cb3dc
workflow graph env-wf2.cwl

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

Path: cwltool/schemas/v1.0/v1.0/env-wf2.cwl

Branch/Commit ID: 65aedc5e7e1f3ccace7f9022f8a54b3f0d5c9a8c
workflow graph HBA_calibrator.cwl

https://git.astron.nl/RD/LINC.git

Path: workflows/HBA_calibrator.cwl

Branch/Commit ID: 8a697be0fa85795f7822146015edf963a5681ca7
workflow graph gathered exome alignment and somatic variant detection

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

Path: definitions/pipelines/gathered_somatic_exome.cwl

Branch/Commit ID: 5cb188131f786ed33156e2f0e3dd63ab9c04245d
workflow graph trimmed_fastq

Quality Control and Raw Data trimming

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

Path: structuralvariants/subworkflows/trimmed_fastq.cwl

Branch/Commit ID: e1fd26587a78afc376c10bf6db36abd2c840768e
workflow graph env-wf3.cwl

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

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

Branch/Commit ID: 7ec307b01442936fad9b1149f4500496557505ff
workflow graph Subworkflow that runs cnvkit in single sample mode and returns a vcf file

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

Path: definitions/subworkflows/cnvkit_single_sample.cwl

Branch/Commit ID: 051074fce4afd9732ef34db9dd43d3a1d8e979d6
workflow graph io-int-default-wf.cwl

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/io-int-default-wf.cwl

Branch/Commit ID: e515226f8ac0f7985cd94dae4a301150adae3050