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: 2c486543c335bb99b245dfe7e2f033f535efb9cf
workflow graph echo-wf-default.cwl

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

Path: cwltool/schemas/v1.0/v1.0/echo-wf-default.cwl

Branch/Commit ID: 3e9bca4e006eae7e9febd76eb9b8292702eba2cb
workflow graph FASTQ to BQSR

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

Path: definitions/subworkflows/fastq_to_bqsr.cwl

Branch/Commit ID: 441b85003fdc10cf4cbf333d89acb4d23b0fef32
workflow graph collapsed_fastq_to_bam.cwl

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/marianas/collapsed_fastq_to_bam.cwl

Branch/Commit ID: 9e6eae9eb8448e68d509397a46303551a93a164d
workflow graph scatter-valuefrom-wf6.cwl

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf6.cwl

Branch/Commit ID: 03af16c9df2ee77485d4ab092cd64ae096d2e71c
workflow graph bam to trimmed fastqs and biscuit alignments

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

Path: definitions/subworkflows/bam_to_trimmed_fastq_and_biscuit_alignments.cwl

Branch/Commit ID: 441b85003fdc10cf4cbf333d89acb4d23b0fef32
workflow graph env-wf1.cwl

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

Path: cwltool/schemas/v1.0/v1.0/env-wf1.cwl

Branch/Commit ID: 3e9bca4e006eae7e9febd76eb9b8292702eba2cb
workflow graph harmonization_bwa_mem_no_trim.cwl

https://github.com/uc-cdis/genomel_pipelines.git

Path: genomel/cwl/workflows/harmonization/harmonization_bwa_mem_no_trim.cwl

Branch/Commit ID: 3c62f1ddf8a54601da3cfc16c37d0cb0d6a2ea28
workflow graph GATKBaseRecalBQSRWorkflow_4_1_3.cwl

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

Path: janis_pipelines/wgs_somatic/cwl/tools/GATKBaseRecalBQSRWorkflow_4_1_3.cwl

Branch/Commit ID: ccca639fe0b3a8104ff9fcfa285f1134706032b8
workflow graph qc-assembled.workflow.cwl

https://github.com/MG-RAST/pipeline.git

Path: CWL/Workflows/qc-assembled.workflow.cwl

Branch/Commit ID: 6c5d0068bdb4f19a36a653c39964aefb9e5a7b1b