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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: d1bef74924efcb8bfaa00987b3f148d5a192b7a9
workflow graph tt_hmmsearch_wnode.cwl

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

Path: task_types/tt_hmmsearch_wnode.cwl

Branch/Commit ID: 803f6367d1b279a7b6dc1a4e8ae43f1bbec9f760
workflow graph ani_top_n

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

Path: task_types/tt_ani_top_n.cwl

Branch/Commit ID: 7f857f7f2d7c080d27c775b67a6d6f7d94bce31f
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: ae75b938e6e8ae777a55686bbacad824b3c6788c
workflow graph kmer_top_n_extract

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: 7f857f7f2d7c080d27c775b67a6d6f7d94bce31f
workflow graph gcaccess_from_list

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

Path: task_types/tt_gcaccess_from_list.cwl

Branch/Commit ID: 1bf7dc7b03ea3c64e54375cc5c3767849a801000
workflow graph WGS QC workflow

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

Path: definitions/subworkflows/qc_wgs.cwl

Branch/Commit ID: f77a920bcc73f6cfdb091eed75a149d02cd8a263
workflow graph tt_kmer_compare_wnode

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: 1bf7dc7b03ea3c64e54375cc5c3767849a801000
workflow graph running cellranger mkfastq and count

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

Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl

Branch/Commit ID: ffd73951157c61c1581d346628d75b61cdd04141
workflow graph conflict.cwl#main

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

Path: tests/wf/conflict.cwl

Branch/Commit ID: 5ae5798f1c0c8d2178986b77cfd74edff510877a

Packed ID: main