DESeq2 protocols

DESeq2 specifications


Unique identifier OMICS_30806
Name DESeq2
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 1.20.0
Stability Stable
Requirements S4Vectors, IRanges, GenomicRanges, SummarizedExperiment
Maintained Yes



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  • person_outline Simon Anders <>

Publication for DESeq2

DESeq2 IN pipelines

PMCID: 5754050
PMID: 29300724
DOI: 10.1371/journal.pgen.1007127

[…] content were removed. low abundance genes (mean count <1) were excluded, as were overrepresented genes (>20% of total sequencing reads). differentially expressed genes were identified using deseq2 (version 1.16.1, bioconductor). prior to differential expression analysis, the data were filtered to remove genes with a dropout rate of higher than 75%; differential expression analysis […]

PMCID: 5758557
PMID: 29354030
DOI: 10.3389/fnmol.2017.00433

[…] was performed using htseq v0.6.1 (anders and huber, 2010) and ensembl release 81 database. supervised statistical analysis for differential gene expression has been performed using r (3.3.2) and the deseq2 bioconductor (v3.2) library. multiple testing was adjusted by benjamini and hochberg fdr correction (benjamini and hochberg, 1995)., after normalization and rlog transformation, hierarchical […]

PMCID: 5776139
PMID: 29386992
DOI: 10.3389/fnmol.2017.00429

[…] genome rnor 6.0 version by using bwa aligner (li et al., 2009). raw read counts were evaluated by samtools software (li et al., 2009). set of differentially expressed (de) genes were estimated by deseq2 (love et al., 2014)., list of genes that are selectively expressed in different cells types was created on the basis of data of single cell rna-seq (zhang et al., 2014; zeisel et al., 2015). […]

PMCID: 5776139
PMID: 29386992
DOI: 10.3389/fnmol.2017.00429

[…] from kegg database. de genes list was analyzed for subsets of genes specific to tissues and specific to selected pathways. in this type of analysis, de genes were selected if adjusted p-value of deseq2 test <0.05., raw read data were published to sra and can be accessed by using range of accession numbers srr5750530–srr5750542., all data are presented as mean ± sem. across groups […]

PMCID: 5777994
PMID: 29358719
DOI: 10.1038/s41598-018-19453-4

[…] reads) obtained from flux capacitor output. a minimum threshold of rpkm ≥10 was used to eliminate low expression transcripts and limit noise. differential expression analysis was carried out via the deseq2 v1.10.1 differential expression analysis package in r., strains used in this study are listed in supplementary table s6. two of the 19 deletion mutants were also part of the holland deletion […]

DESeq2 institution(s)
Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute and 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
DESeq2 funding source(s)
Supported by the International Max Planck Research School for Computational Biology and Scientific Computing and a grant from the National Institutes of Health (5T32CA009337-33) and by the European Union’s 7th Framework Programme (Health) via Project Radiant.

DESeq2 review

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Gyan Prakash Mishra

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Most widely and efficient tool for RNASeq data analysis. The key feature of this tool compared to RPKM based tool is that it models data accounting for several factors ( e.g Batch effect ). If your data includes some interaction terms like multiple condition (e. g treated ,untreated) in multiple genotypes (e.g WT, KO ), you can actually design the model and contrast based on your question.