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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: 9ee330737f4603e4e959ffe786fbb2046db70a00
workflow graph Create Genomic Collection for Bacterial Pipeline

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

Path: genomic_source/wf_genomic_source.cwl

Branch/Commit ID: 49732e54e2fe2eafd2f82df3c482c73e642f6d64
workflow graph CreateSymlink-workflow.cwl

https://github.com/nal-i5k/organism_onboarding.git

Path: CreateSymlink-workflow.cwl

Branch/Commit ID: 3aabbb0f6635bb9354ad52f616ab7cfc61848eb6
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: 6003cbb94f16103241b562f2133e7c4acac6c621
workflow graph bact_get_kmer_reference

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

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: 122aba2dafbb63241413c82b725b877c04523aaf
workflow graph DiffBind - Differential Binding Analysis of ChIP-Seq Peak Data

Differential Binding Analysis of ChIP-Seq Peak Data --------------------------------------------------- DiffBind processes ChIP-Seq data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by ChIP-Seq peak callers and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4.

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

Path: workflows/diffbind.cwl

Branch/Commit ID: b5e16e359007150647b14dc6e038f4eb8dccda79
workflow graph gp_makeblastdb

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

Path: progs/gp_makeblastdb.cwl

Branch/Commit ID: 9abcceb95c8dcb74b98d7eeda265d8f3a9a4329e
workflow graph Single-Cell RNA-Seq Differential Expression Analysis

Single-Cell RNA-Seq Differential Expression Analysis Identifies differentially expressed genes between any two groups of cells, optionally aggregating gene expression data from single-cell to pseudobulk form.

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

Path: workflows/sc-rna-de-pseudobulk.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16
workflow graph iwdr_with_nested_dirs.cwl

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

Path: cwltool/schemas/v1.0/v1.0/iwdr_with_nested_dirs.cwl

Branch/Commit ID: a3d565bf8e630101d25d31804cfbceb0a0ba28de
workflow graph conflict-wf.cwl#collision

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

Path: cwltool/schemas/v1.0/v1.0/conflict-wf.cwl

Branch/Commit ID: e8b3565a008d95859fc44227987a54e6a53a8c29

Packed ID: collision