DESeq specifications


Unique identifier OMICS_01306
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

DESeq article

DESeq citations

PMCID: 5955993

[…] using tophat268. number of reads mapped to each gene feature was first quantified using htseq69 with the input of gtf files obtained from the ucsc table browser and then normalized using deseq270. differential expression analysis was done with deseq2. p-values were adjusted using the benjamini–hochberg procedure. genes with adjusted differential expression p-values smaller than 0.05 […]

PMCID: 5952855

[…] fastq files were aligned to the uscsrn5 rattus norvegicus reference genome with star aligner [38] with allowed mismatches set to 14. differentially expressed (de) mrnas were determined using the deseq2 package based on the negative binomial distribution and a false discovery rate of 0.1% [39]. in brief, paired rnaseq data for each transcript are compared using wald testing […]

PMCID: 5933715

[…] (release 67), thus obtaining counts for rna-seq reads that mapped unambiguously to ensembl gene models. these rna-seq counts were used for differential gene expression analysis performed with deseq2. for the differential gene expression analysis, a comparison of rna-seq reads counts between two experimental conditions was done, with each condition containing rna-seq data collected […]

PMCID: 5946031

[…] of gene per million mapped fragments) to obtain the relative expression levels. differential expression analysis between wt and nc117 overexpression strains (wt-pja2-nc117) was performed using the deseq2 software (anders and huber, 2010), which used a model based on the negative binomial distribution. the resulting p-values were adjusted using the benjamini and hochberg’s approach […]

PMCID: 5928111

[…] were normalized by using tpm (transcripts per million) algorithm18: normalized expression = (mapped reads)/(total reads) * 1000000. differential expression of two samples was performed by using deseq. 226. p-values was adjusted by benjamini & hochberg method27. by default, the threshold of corrected p-value for differential expression was set to 0.05., gene ontology (go) enrichment […]

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.

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