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RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
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Related Experiment Video

Updated: May 8, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Count-based differential expression analysis of RNA sequencing data using R and Bioconductor.

Simon Anders¹, Davis J McCarthy, Yunshun Chen

¹Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.

Nature Protocols

|August 27, 2013

Summary

This protocol provides a best-practice workflow for RNA sequencing (RNA-seq) differential gene expression analysis. It guides users through crucial steps using R and Bioconductor tools like DESeq and edgeR for efficient transcriptome profiling.

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Last Updated: May 8, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Published on: June 24, 2021

Area of Science:

Genomics and Bioinformatics
Molecular Biology

Background:

RNA sequencing (RNA-seq) is vital for transcriptome profiling in gene regulation, development, and disease studies.
Identifying differentially expressed genes across conditions requires careful consideration of statistical modeling and data variability.

Purpose of the Study:

To present a state-of-the-art computational and statistical workflow for RNA-seq differential expression analysis.
To provide guidance on current best practices for analyzing RNA-seq data.

Main Methods:

Utilizes the R language and Bioconductor software, specifically the DESeq and edgeR tools.
Covers essential aspects including read counting, biological variability, quality control, and statistical modeling.
Presents a protocol for differential gene expression analysis in RNA sequencing.

Main Results:

The protocol offers a robust workflow for differential gene expression analysis.
Hands-on time for small experiments is less than 1 hour, with computation time under 1 day on a standard PC.

Conclusions:

This protocol establishes a best-practice, efficient workflow for RNA-seq differential expression analysis.
The workflow is accessible, utilizing free, open-source software for broad applicability in biological research.