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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...

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Updated: Feb 22, 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

RNA-Seq Data Analysis: From Raw Data Quality Control to Differential Expression Analysis.

Weihong Qi¹, Ralph Schlapbach², Hubert Rehrauer³

¹Functional Genomics Center Zurich, Winterthurerstr. 190, Y32H66, 8057, Zurich, Switzerland. Weihong.qi@fgcz.ethz.ch.

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

|September 23, 2017

Summary

This summary is machine-generated.

This study presents a standardized RNA sequencing (RNA-seq) data analysis protocol to identify differentially expressed genes between two conditions. The protocol ensures reliable interpretation and is automated in the ezRun R package for new users.

Keywords:

Differential gene expression Gene expression quantification Quality control RNA-seq Read alignment

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Area of Science:

Life Sciences
Genomics
Bioinformatics

Background:

RNA sequencing (RNA-seq) is a powerful technology for life sciences research.
RNA-seq data analysis involves complex computational pipelines with numerous variations.
Standardized protocols are needed for reliable gene expression analysis.

Purpose of the Study:

To describe a protocol for RNA-seq data analysis focused on identifying differentially expressed genes.
To provide a guide for new RNA-seq users to understand basic analysis steps.
To ensure reliable data interpretation through quality checkpoints.

Main Methods:

The protocol follows RNA-seq data analysis best practices.
Quality checkpoints are integrated throughout the analysis pipeline.
An automated workflow is implemented in the R package ezRun and SUSHI framework.

Main Results:

The protocol facilitates the identification of differentially expressed genes between two conditions.
Automated workflows in ezRun and SUSHI provide repeatable and interpretable results.
The ezRun package simplifies RNA-seq data analysis for new users.

Conclusions:

This protocol offers a reliable and standardized approach to RNA-seq data analysis.
The ezRun R package enhances the accessibility and reproducibility of RNA-seq studies.
Adherence to best practices and quality control ensures trustworthy gene expression findings.