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Graph	Name	Retrieved From	View
	scatter-valuefrom-wf3.cwl#main	https://github.com/common-workflow-language/cwltool.git Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf3.cwl Branch/Commit ID: 478c2ffc09fb189c4f36ccb82aad945b3db5f9b3 Packed ID: main
	run-openfoam.cwl	https://github.com/chgarth/cwl-openfoam.git Path: cwl/run-openfoam.cwl Branch/Commit ID: 2fbe585034c501b7cc8a20ad495d8eeb86b7ba3d
	dynresreq-workflow.cwl	https://github.com/common-workflow-language/cwltool.git Path: cwltool/schemas/v1.0/v1.0/dynresreq-workflow.cwl Branch/Commit ID: bbe20f54deea92d9c9cd38cb1f23c4423133d3de
	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: a0b22644ca178b640fb74849d23b7c631022f0b5
	vcf_concat.cwl	https://github.com/andurill/ACCESS-Pipeline.git Path: workflows/subworkflows/vcf_concat.cwl Branch/Commit ID: 3441040dfaecba58150c13a95a6a93657b00778a
	allele-process-reference.cwl	https://github.com/datirium/workflows.git Path: subworkflows/allele-process-reference.cwl Branch/Commit ID: 01c6af7a598eb44f6bcaa9b5eecf13229f28546e
	Bisulfite alignment and QC	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/bisulfite.cwl Branch/Commit ID: ae79bc51e8b502164dbe74ea3b068d6d4d36a1f8
	Subworkflow to allow calling cnvkit with cram instead of bam files	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/cram_to_cnvkit.cwl Branch/Commit ID: 24e5290aec441665c6976ee3ee8ae3574c49c6b5
	wf_rescue_ratio_1input.cwl Calculates the rescue ratio (see Gabe's protocols paper), given two eCLIP IP samples and 2 size-matched input samples. Also returns the reproducible peaks given these two samples. This is different from the 1input workflow in that each INPUT is first merged together and is used downstream instead of the 1input version, which remains unmodified. Merged inputs are NOT used in calculating true reproducible peaks.	https://github.com/YeoLab/merge_peaks.git Path: cwl/wf_rescue_ratio_1input.cwl Branch/Commit ID: 18933d4d4b00e97a8a0d155abbebad1fdbc254aa
	bam_readcount workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/bam_readcount.cwl Branch/Commit ID: e2a34d2b8c406db9aed8e49e8bdcf36f51444379