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Related Experiment Video

Updated: Jan 4, 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

Fully Bayesian analysis of allele-specific RNA-seq data.

Ignacio Alvarez-Castro¹, Jarad Niemi²

¹Instituto de Estadística, Universidad de la República, Montevideo, Uruguay.

Mathematical Biosciences and Engineering : MBE

|November 9, 2019

Summary

This study introduces a new statistical model to analyze allele-specific expression (ASE) from RNA sequencing data. The method accurately detects differential allelic expression, accounting for unique ASE challenges like allele bias.

Keywords:

allele-specific expression GPU Markov chain Monte Carlo RNA-seq hierarchical model shrinkage priors

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

Diploid organisms possess two gene copies (alleles) with distinct RNA abundance, known as allele-specific expression (ASE).
Studying ASE using RNA-seq requires specialized statistical methods due to unique characteristics like allele bias and increased complexity in gene expression modeling.

Purpose of the Study:

To develop and present advanced statistical methods for modeling allele-specific expression (ASE).
To introduce a robust approach for detecting genes exhibiting differential allelic expression.

Main Methods:

A hierarchical, overdispersed, count regression model was developed to handle ASE count data.
The model incorporates gene-specific overdispersion and an internal measure for reference allele bias.
Fully Bayesian inference was implemented using the fbseq package with a parallel strategy for computational efficiency.

Main Results:

The proposed model effectively accommodates gene-specific variations and quantifies reference allele bias.
The fbseq package provides a computationally efficient framework for analyzing ASE data.
Simulations and real data analyses confirm the model's practicality and power in differential ASE studies.

Conclusions:

The presented statistical framework offers a powerful and practical solution for analyzing allele-specific expression.
This approach enhances the ability to detect differential allelic expression, advancing genomic research.