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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: ee66d03be8a7fd61367db40c37a973ff55ece4da
workflow graph ST520108.cwl

https://github.com/Marco-Salvi/dtc51.git

Path: ST520108.cwl

Branch/Commit ID: 272db37d2b8108a146769f0fb0383bb824c9788f
workflow graph fp_filter workflow

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

Path: definitions/subworkflows/fp_filter.cwl

Branch/Commit ID: 31602b94b34ff55876147c7299e1bec47e8d1a31
workflow graph picard_markduplicates

Mark duplicates

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

Path: structuralvariants/subworkflows/picard_markduplicates.cwl

Branch/Commit ID: 637e294ff72687314faacef2c30cb46874611e50
workflow graph ST520112.cwl

https://github.com/Marco-Salvi/dtc51.git

Path: ST520112.cwl

Branch/Commit ID: 272db37d2b8108a146769f0fb0383bb824c9788f
workflow graph ST520110.cwl

https://github.com/Marco-Salvi/dtc51.git

Path: ST520110.cwl

Branch/Commit ID: 272db37d2b8108a146769f0fb0383bb824c9788f
workflow graph Single-Cell ATAC-Seq Genome Coverage

Single-Cell ATAC-Seq Genome Coverage Generates genome coverage tracks from chromatin accessibility data of selected cells

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

Path: workflows/sc-atac-coverage.cwl

Branch/Commit ID: 93b844a80f4008cc973ea9b5efedaff32a343895
workflow graph cluster_blastp_wnode and gpx_qdump combined

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

Path: task_types/tt_cluster_and_qdump.cwl

Branch/Commit ID: f403d9e26d60d3e3591a03077bc9dfa188b1c2bb
workflow graph Chipseq alignment for nonhuman with qc and creating homer tag directory

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

Path: definitions/pipelines/chipseq_alignment_nonhuman.cwl

Branch/Commit ID: 788bdc99c1d5b6ee7c431c3c011eb30d385c1370
workflow graph Detect DoCM variants

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

Path: definitions/subworkflows/docm_germline.cwl

Branch/Commit ID: 0c4f4e59c265eb22aed3d2d37b173cb5430773d2