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Updated: Apr 3, 2026

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

A two-phase clustering procedure based on allele specific expression.

Roberto Pagliarini¹, Francesco Nascimben², Alberto Policriti²

¹Department of Mathematics, Computer Science, and Physics, University of Udine, Via delle Scienze 206, 33100, Udine, Italy. roberto.pagliarini@uniud.it.

BMC Bioinformatics

|April 2, 2026

Summary

This summary is machine-generated.

This study introduces a novel two-phase clustering method for allele-specific expression (ASE) analysis. The developed algorithm effectively groups individuals based on their allelic expression patterns, outperforming standard techniques.

Keywords:

Cis-regulatory diversity Allele specific expression analysis Spectral clustering Unsupervised clustering

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Last Updated: Apr 3, 2026

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

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Genomics
Transcriptomics
Bioinformatics

Background:

Allele Specific Expression (ASE) analysis integrates genome and transcriptome data.
ASE quantifies expression variation between haplotypes to estimate cis-regulatory diversity.
Clustering algorithms can identify patterns in gene/sample expression profiles.

Purpose of the Study:

To develop a novel unsupervised clustering procedure for allele-specific expression data.
To partition populations into groups based on similar allelic expression profiles.
To address the lack of ad-hoc procedures for ASE data clustering.

Main Methods:

Defined an expression matrix from RNA-sequencing data for allele expressions.
Developed a two-phase unsupervised clustering procedure.
Utilized a spectral clustering algorithm as the foundation for the procedure.

Main Results:

Successfully clustered 98 Vitis vinifera cultivars based on chromosome 1 gene expression.
Analyzed allele-specific count data from a CASTxMRL F1 hybrid mice dataset.
Demonstrated the algorithm's ability to partition populations by allelic expression.

Conclusions:

The novel clustering algorithm shows significant robustness.
The developed procedure outperforms standard clustering techniques.
Validated through real-world case studies and synthetic data.