DESeq2 statistics

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Citations per year

Number of citations per year for the bioinformatics software tool DESeq2
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Tool usage distribution map

This map represents all the scientific publications referring to DESeq2 per scientific context
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Associated diseases

This word cloud represents DESeq2 usage per disease context
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Popular tool citations

chevron_left Differential expression Differential abundant feature detection Gene set enrichment analysis Normalization Normalization Known transcript quantification Differential expression detection Table of counts chevron_right
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Protocols

DESeq2 specifications

Information


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
methods, RColorBrewer, Biobase, IRanges, GenomicRanges, testthat, locfit, ggplot2, BiocParallel, S4Vectors(>=0.9.25), rmarkdown, knitr, genefilter, vsn, SummarizedExperiment(>=1.1.6), Hmisc, BiocGenerics(>=0.7.5), geneplotter, Rcpp(>=0.11.0), pheatmap, IHW, apeglm, ashr, tximport, tximportData, readr, pbapply, airway, pasilla(>=0.2.10)
Maintained Yes

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Versioning


No version available

Documentation


Maintainer


  • person_outline Simon Anders

Publication for DESeq2

DESeq2 citations

 (1979)
library_books

MLL fusion driven leukemia requires SETD2 to safeguard genomic integrity

2018
Nat Commun
PMCID: 5959866
PMID: 29777171
DOI: 10.1038/s41467-018-04329-y

[…] ing of the alignment results with SAMtools (0.1.19) counts per gene were obtained by HTSeq (version 0.6.0). Normalization and differential expression analysis between two samples was carried out with DESeq2. For the visualization of gene expression and unsupervised hierarchical clustering of the samples the rlog normalization in DESeq2 was applied. We used the R library pheatmap for sample cluster […]

call_split

FACS Seq analysis of Pax3 derived cells identifies non myogenic lineages in the embryonic forelimb

2018
Sci Rep
PMCID: 5956100
PMID: 29769607
DOI: 10.1038/s41598-018-25998-1
call_split See protocol

[…] reads TopHat version 2.1.0 was used to align all reads to the mm10 genome with default parameters and to identify splice junctions,. HTseq was used to create count tables from tophat2 aligned reads. DESeq2 was used to calculate differential gene expression between time points using an FDR adjusted cutoff of p ≤ 0.05, with a fold change ≥1.5, between any two consecutive time points. Principal comp […]

call_split

Methionine metabolism influences genomic architecture and gene expression through H3K4me3 peak width

2018
Nat Commun
PMCID: 5955993
PMID: 29769529
DOI: 10.1038/s41467-018-04426-y
call_split See protocol

[…] 8, respectively, using TopHat2. Number of reads mapped to each gene feature was first quantified using HTSeq with the input of GTF files obtained from the UCSC Table Browser and then normalized using DESeq2. 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 wer […]

library_books

Comprehensive epigenetic landscape of rheumatoid arthritis fibroblast like synoviocytes

2018
Nat Commun
PMCID: 5953939
PMID: 29765031
DOI: 10.1038/s41467-018-04310-9

[…] less than 30 bp were further discarded. The remaining reads were aligned to human reference genome hg19 using STAR (2.3.0) and assembled and quantified by HTSeq (0.5.4p5). DEGs were identified using DESeq2 package in R. To be considered a DEG, twofold change of gene expression levels between RA and OA should be achieved and the B-H adjusted p-value is <0.05. For the following analysis, transcript […]

library_books

Gestational diabetes is associated with change in the gut microbiota composition in third trimester of pregnancy and postpartum

2018
Microbiome
PMCID: 5952429
PMID: 29764499
DOI: 10.1186/s40168-018-0472-x

[…] .To assess the cross-sectional differences at OTU level, we performed differential abundance analyses on unrarefied, untransformed OTU tables using a negative binomial Wald test as implemented in the DESeq2 R package []. Only OTUs present in at least 10% of samples and with a mean proportional abundance of 0.01% (561 of 5464 OTUs) were considered. OTUs exhibiting a differential change from pregnan […]

library_books

Interaction between Host MicroRNAs and the Gut Microbiota in Colorectal Cancer

2018
mSystems
PMCID: 5954203
DOI: 10.1128/mSystems.00205-17

[…] We identified differentially expressed (DE) miRNAs between tumor and normal samples using the DESeq2 package (1.10.1) in R (version 3.2.3) (). Raw miRNA counts were filtered to include miRNAs with ≥1 read in ≥80% of the samples. The remaining 392 miRNAs were then used for DESeq2 analysis. We d […]


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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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Desktop
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.