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DESeq | Differential expression analysis for sequence count data

Performs differential gene expression analysis. DEseq is a method that integrates methodological advances with features to facilitate quantitative analysis of comparative RNA-seq data using shrinkage estimators for dispersion and fold change. The software is suitable for small studies with few replicates as well as for large observational studies. Its heuristics for outlier detection assist in recognizing genes for which the modeling assumptions are unsuitable and so avoids type-I errors caused by these.

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2 user reviews

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2 user reviews

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Arup Ghosh's avatar image Arup Ghosh's country flag

Arup Ghosh

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Deseq2 is a fast and powerful R package for differential gene expression analysis using raw read counts.

Claudia Armenise Quartz Bio's avatar image

Claudia Armenise Quartz Bio

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Very powerful R package for differential expression analyses. The new implementation, DESeq2, appears to be one of the most relevant approach to identify differentially expressed genes. Cons: As always it requires to use eSet-like classes

DESeq forum

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DESeq classification

DESeq specifications

Unique identifier:
OMICS_01306
Software type:
Package/Module
Restrictions to use:
None
Programming languages:
R
Computer skills:
Advanced
Stability:
Stable
Maintained:
Yes
Alternative name:
DESeq2
Interface:
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
License:
GNU Lesser General Public License version 3.0
Version:
1.18.1
Requirements:
S4Vectors, IRanges, GenomicRanges, SummarizedExperiment

DESeq distribution

versioning

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No versioning.

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DESeq support

Documentation

Maintainers

  • Michael Love <>
  • Simon Anders <>

Additional information

An implementation for detection of differential translated genes using Ribo-seq is available at https://github.com/SGDDNB/DTG-detection

Credits

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Publications

Institution(s)

Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA, USA; Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA; Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany

Funding source(s)

Supported by a stipend from the International Max Planck Research School for Computational Biology and Scientific Computing, a grant from the National Institutes of Health (5T32CA009337-33), and the European Union’s 7th Framework Programme (Health) via Project Radiant.

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