DESeq specifications

Unique identifier:
OMICS_01306
Software type:
Package/Module
Restrictions to use:
None
Programming languages:
R
Computer skills:
Advanced
Stability:
Stable
Maintained:
Yes
Alternative name:
DESeq2
Interface:
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
License:
GNU Lesser General Public License version 3.0
Version:
1.18.1
Requirements:
S4Vectors, IRanges, GenomicRanges, SummarizedExperiment

versioning

tutorial arrow
×
Upload and version your source code
Get a DOI for each update to improve tool traceability. Archive your releases so the community can easily visualize progress on your work.
Facilitate your tool traceability
Sign up for free to upload your code and get a DOI

No versioning.

DESeq distribution

download

DESeq support

Documentation

Maintainers

  • Michael Love <>
  • Simon Anders <>

Additional information

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

forum

tutorial arrow
×
Communicate with other users
Participate in the forum to get support for using tools. Ask questions about technical specifications.
Take part in the discussion
Sign up for free to ask question and share your advices

No open topic.

Credits

tutorial arrow
×
Promote your skills
Define all the tasks you managed and assign your profile the appropriate badges. Become an active member.
Promote your work
Sign up for free to badge your contributorship

Publications

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

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.

User review

tutorial arrow
×
Vote up tools and offer feedback
Give value to tools and make your expertise visible
Give your feedback on this tool
Sign up for free to join and share with the community
Sort by:

4 user reviews

star_border star_border star_border star_border star_border
star star star star star

4 user reviews

star_border star_border star_border star_border star_border
star star star star star
Thyago's avatar image Thyago's country flag

Thyago

star_border star_border star_border star_border star_border
star star star star star

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's avatar image Dr Nick's country flag

Dr Nick

star_border star_border star_border star_border star_border
star star star star star

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.

Arup Ghosh's avatar image Arup Ghosh's country flag

Arup Ghosh

star_border star_border star_border star_border star_border
star star star star star

Deseq2 is a fast and powerful R package for differential gene expression analysis using raw read counts.

Claudia Armenise Quartz Bio's avatar image

Claudia Armenise Quartz Bio

star_border star_border star_border star_border star_border
star star star star star

Very powerful R package for differential expression analyses. The new implementation, DESeq2, appears to be one of the most relevant approach to identify differentially expressed genes. Cons: As always it requires to use eSet-like classes

Related Tools