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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...

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Updated: Jun 10, 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

baySeq: empirical Bayesian methods for identifying differential expression in sequence count data.

Thomas J Hardcastle¹, Krystyna A Kelly

¹Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, UK. tjh48@cam.ac.uk

BMC Bioinformatics

|August 12, 2010

Summary

This summary is machine-generated.

We developed baySeq, a novel algorithm for analyzing high throughput sequencing data. This empirical Bayes method accurately detects differential gene expression patterns, outperforming existing approaches.

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Area of Science:

Genomics and Transcriptomics
Bioinformatics and Computational Biology

Background:

High-throughput sequencing is crucial for analyzing genomic and transcriptomic data, particularly for expression levels.
Identifying patterns of differential gene expression is a key step for further analysis and validation in sequencing studies.

Purpose of the Study:

To propose a framework for defining differential expression patterns.
To develop and evaluate a novel algorithm, baySeq, for detecting these patterns in sequencing data.

Main Methods:

Developed baySeq, an algorithm employing an empirical Bayes approach.
Assumed a negative binomial distribution for sequencing count data.
Derived an empirically determined prior distribution from the entire dataset.

Main Results:

baySeq demonstrated strong performance on both real and simulated sequencing data.
The method achieved comparable or superior results to existing methods for pairwise differential expression analysis.
Significant performance gains were observed when analyzing data with multiple sample groups.

Conclusions:

The baySeq approach represents a significant advancement for analyzing count data from sequencing experiments.
The empirical Bayes method effectively identifies differential expression patterns, enhancing biological discovery.