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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.
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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.
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Related Experiment Video

Updated: Sep 6, 2025

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

Sequences to Differences in Gene Expression: Analysis of RNA-Seq Data.

Polina V Pavlovich¹, Pierre Cauchy^2,3

¹Moscow Institute of Physics and Technology, Dolgoprudny, Russia.

Methods in Molecular Biology (Clifton, N.J.)

|June 23, 2022

Summary

This summary is machine-generated.

This chapter details RNA sequencing (RNA-Seq) analysis, covering protocols and tools for gene expression measurement. It provides a step-by-step workflow with code and statistics for analyzing RNA-Seq data.

Keywords:

Alignment Bioinformatics Differential expression Pipeline Protocols RNA-Seq Statistics

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Area of Science:

Genomics
Bioinformatics

Background:

RNA sequencing (RNA-Seq) is a standard method for gene expression analysis.
The development of analytical tools has paralleled the maturation of RNA-Seq technology over the past decade.

Purpose of the Study:

To describe mainstream and up-to-date RNA-Seq protocols and their impact on downstream analysis.
To detail the stages of RNA-Seq analysis: preprocessing, upstream processing, and high-level analyses.
To review current tools within a cohesive workflow, including their features, required code, and underlying statistics.

Main Methods:

A comprehensive review of RNA-Seq analysis protocols and tools.
Stepwise detailing of the RNA-Seq analysis workflow.
Inclusion of code and statistical explanations.
Illustration with publicly available RNA-Seq data.

Main Results:

A structured overview of RNA-Seq analysis from data preparation to high-level interpretation.
Introduction to the latest analytical tools and their functionalities.
Practical guidance through code and statistical underpinnings.

Conclusions:

This chapter offers a practical guide to RNA-Seq data analysis, integrating protocols, tools, and statistical methods.
It serves as a resource for researchers to effectively perform and interpret gene expression studies using RNA-Seq.