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RNA-seq

RNA-seq

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.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

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Updated: May 20, 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

Statistical methods for identifying differentially expressed genes in RNA-Seq experiments.

Zhide Fang¹, Jeffrey Martin, Zhong Wang

¹Biostatistics Program, School of Public Health, LSU Health Sciences Center, 2020 Gravier Street, 3rd floor, New Orleans, LA, 70112, USA. zfang@lsuhsc.edu.

Cell & Bioscience

|August 2, 2012

Summary

This summary is machine-generated.

RNA sequencing (RNA-Seq) offers superior gene expression profiling. This review guides selecting statistical methods for identifying differentially expressed transcripts between experimental conditions using RNA-Seq data.

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Area of Science:

Bioinformatics
Genomics
Statistical analysis

Background:

RNA sequencing (RNA-Seq) is increasingly preferred over microarrays for gene expression profiling due to enhanced accuracy and sensitivity.
Identifying differentially expressed transcripts between experimental conditions is a common challenge in gene profiling.

Purpose of the Study:

To provide a comprehensive review of statistical methods for RNA-Seq data analysis.
To guide researchers in selecting appropriate metrics for identifying differential gene expression.

Main Methods:

Review of existing statistical methods for microarray data analysis applicable to RNA-Seq.
In-depth review of newly developed statistical methods specifically designed for RNA-Seq data.

Main Results:

Existing statistical methods may require modifications for RNA-Seq data.
Several novel statistical approaches have been developed exclusively for RNA-Seq datasets.

Conclusions:

Choosing the right statistical method is crucial for accurate differential gene expression analysis in RNA-Seq.
This review serves as a guide for researchers navigating the landscape of RNA-Seq statistical analysis tools.