DESeq pipeline

DESeq specifications


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



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  • person_outline Michael Love <>
  • person_outline Simon Anders <>

Additional information

An implementation for detection of differential translated genes using Ribo-seq is available at

Publication for DESeq

DESeq citations

PMCID: 5775430

[…] degs between groups, many genes with similar expression pattern among different groups were filtered out due to insignificance. therefore, we normalized the read counts from htseq using the deseq. 2 package, and performed weighted gene co-expression network analysis (wgnca) to cluster genes into different modules based on their expression patterns. a total of 19 modules containing 33 […]

PMCID: 5800670

[…] levels. fpkm of each gene was calculated based on the length of the gene and reads count mapped to this gene., differential expression analysis was conducted using the deseq r package (1.18.0). deseq facilitates accurate comparisons between antioxidant enzyme activity of liver tissues by normalizing the number of reads, and provides statistical routines for determining differential […]

PMCID: 5824801

[…] them, were excluded prior to differential expression analysis to improve the statistical power. count data were imported in r (version 3.2.0) and the analysis of differential expression conducted in deseq. 2 (version: 1.8.1)151, an r bioconductor package152. samples of both species were grouped according to sex and expression was compared separately for brains and gonads, following […]

PMCID: 5897438

[…] for estimating gene expression levels, was calculated based on the length of the gene and reads count mapped to this gene. differential expression analysis of two groups was performed using the deseq r package (1.18.0). log2(fold change) of 1 and p-value of 0.05 were set as the threshold for significantly differential expression., the gene expression data derived from rna-seq was used […]

PMCID: 5915582

[…] reference genome (grcm38) using tophat2 software50. gene expression levels were estimated using fpkm (fragments per kilobase of exon per million fragments mapped) values using cufflinks software51., deseq.52 and q-value were employed to evaluate differential gene expression between groups. after that, gene abundance differences between those samples were calculated based on the ratio of fpkm […]

DESeq 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
DESeq 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.

DESeq reviews

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DEseq2 is a very robust and fast package for differential gene expression analysis of RNA-seq data. It has a very intuitive pipeline to get most out of your data with few lines of code. The documentation is also pretty straightforward, I would only include more Case Studies, since the technique is used with many diverse experimental designs. On the other hand, Bioconductor mailing list and forums frequented by the author(s) help circuvemting this. In summary, is a must tool for a bioinformatician toolkit.

Dr Nick

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A very robust algorithm and streamlined package for that final step in RNA-seq pipelines--differential expression analyses. Note the current iteration, DESeq2 provides improved considerations for normalization and transformation of feature counts. Several tutorials exist on how to get started using the package for bulk RNA-seq, but have personally found some disagreement on best practices for its use with single cell RNA-seq.