DESeq2 protocols

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DESeq2 statistics

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


  • person_outline Simon Anders <>

Publication for DESeq2

DESeq2 in pipelines

 (558)
2018
PMCID: 5753437
PMID: 29298676
DOI: 10.1186/s12864-017-4411-1

[…] []. the number of reads aligning to annotated gene models was determined using htseq []. read counts were normalized with a variance stabilizing transformation (vst) implemented in the r-package deseq2 []. these gene expression values were used in further downstream analyses., quantitative pcr (qpcr) analysis was run for rna samples from three replicate trees per genotype after a dnase […]

2018
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 […]

2018
PMCID: 5755313
PMID: 29301490
DOI: 10.1186/s12864-017-4392-0

[…] exact test implemented using the go_mwu package [], the package and instructions are available at https://github.com/z0on/go_mwu., to identify genes associated with competence we used two models in deseq2 []. it is important to clarify that gene expression was measured in larvae that were never exposed to a settlement cue but were sampled at the same time and from the same culture vessels […]

2018
PMCID: 5755313
PMID: 29301490
DOI: 10.1186/s12864-017-4392-0

[…] observed in the competence profile after day 7: it compared gene expression on days 8 and 11 (competence peaks) with days 10 and 12 (competence dips). the p-values were obtained using a wald test in deseq2 and the 10% false discovery rate threshold was calculated using independent filtering procedure incorporated into deseq2 pipeline []. gene ontology enrichment was performed using the stat […]

2018
PMCID: 5755908
PMID: 29304067
DOI: 10.1371/journal.pone.0190685

[…] mapping reads (mapq < 10) were filtered out using samtools 1.3.1 []. analysis of differential gene expression was performed using r 3.2.3 (www.r-project.org) and the bioconductor package deseq2 [,] at a 5% false discovery rate. significance of overlaps between lists of degs was determined by fisher’s exact test (function fisher.test()); antagonistic regulation of rpl22bδ degs […]


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DESeq2 in publications

 (2023)
PMCID: 5959866
PMID: 29777171
DOI: 10.1038/s41467-018-04329-y

[…] 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 […]

PMCID: 5956100
PMID: 29769607
DOI: 10.1038/s41598-018-25998-1

[…] 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 […]

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 […]

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

[…] clustering of mirna data by tumor and normal samples (; see also materials and methods below). to identify small rnas that are de between tumor and normal samples, we performed de analysis using deseq2 (see materials and methods). a total of 76 de mirnas were identified, with 55 upregulated and 21 downregulated in tumor tissues compared to normal tissues (p value < 0.05 […]

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, […]


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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.