DESeq protocols

View
settings
DESeq computational protocol

DESeq specifications

Information


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 1.20.0
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

Download


Versioning


Add your version

Documentation


Maintainers


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

Additional information


An implementation for detection of differential translated genes using Ribo-seq is available at https://github.com/SGDDNB/DTG-detection

Publication for DESeq

DESeq IN pipelines

 (449)
2018
PMCID: 5761711
PMID: 29295970
DOI: 10.12659/MSM.905410

[…] we identified the significant demrnas and demirnas in gastric cancer samples compared with the normal samples. a total of 2024 differentially expressed mrnas and 198 mirnas were identified by the “deseq” package in r. then, the heat map with complete linkage clustering of demrnas and demirnas was built using the “gplots” package in r. (supplementary figures 1, 2). as a result, there were 1042 […]

2018
PMCID: 5761878
PMID: 29320569
DOI: 10.1371/journal.pone.0190175

[…] the correlation coefficients (r2) between replicates were calculated using pearson correlation. subsequently, the differential expression detection of genes across libraries was analyzed using the deseq r package (1.10.1) [30]. the p values were adjusted using the benjamini and hochberg method [31]. an adjusted p value (padj) <0.05 found by deseq and |log (fold change)| >1 constituted […]

2018
PMCID: 5761878
PMID: 29320569
DOI: 10.1371/journal.pone.0190175

[…] of genes across libraries was analyzed using the deseq r package (1.10.1) [30]. the p values were adjusted using the benjamini and hochberg method [31]. an adjusted p value (padj) <0.05 found by deseq and |log (fold change)| >1 constituted the threshold to judge the significance of differences in gene expression across libraries. furthermore, go enrichment analysis of the degs […]

2018
PMCID: 5775430
PMID: 29352240
DOI: 10.1038/s41598-018-19754-8

[…] 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 […]

2018
PMCID: 5789592
PMID: 29378511
DOI: 10.1186/s12870-018-1239-z

[…] length bias was corrected. the go terms with degs (fdr ≤ 0.001 and a fold change ≥2) were used for functional enrichment analysis. genes with an adjusted p-value below 0.05, as determined by the deseq software, were assigned as differentially expressed, and employed in the go and kyoto encyclopedia of genes and genomes (kegg) analyses. the kegg enrichment analysis of degs in the kegg […]

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

 (4)
star_border star_border star_border star_border star_border
star star star star star

Thyago

star_border star_border star_border star_border star_border
star star star star star
Desktop
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

star_border star_border star_border star_border star_border
star star star star star
Desktop
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.