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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

Tools and best practices for data processing in allelic expression analysis.

Stephane E Castel^1,2, Ami Levy-Moonshine³, Pejman Mohammadi^4,5

¹New York Genome Center, New York, NY, USA. scastel@nygenome.org.

|September 19, 2015

Summary

This summary is machine-generated.

Allelic expression analysis integrates genome and transcriptome data. This study identifies and corrects technical errors in RNA-sequencing data to improve the detection of allelic expression.

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Area of Science:

Genomics
Transcriptomics
Bioinformatics

Background:

Allelic expression analysis is crucial for understanding gene regulation and variations.
Integrating genome and transcriptome data requires robust analysis of allelic read counts.
Technical errors can confound allelic expression studies.

Purpose of the Study:

To analyze properties of allelic expression read count data.
To identify and characterize technical sources of error in RNA-sequencing data.
To provide guidelines and tools for correcting these errors and improving allelic expression detection.

Main Methods:

Analysis of allelic expression read count data properties.
Identification of technical error sources: low-quality reads, genotyping errors, mapping bias, sample preparation, sequencing, and read depth variation.
Development of quality control measures and correction guidelines.
Introduction of tools for high-throughput allelic expression data production.

Main Results:

Characterization of technical error sources impacting allelic expression analysis.
Demonstration that implemented quality control measures enhance the detection of relevant allelic expression.
Development of a toolkit for efficient processing of RNA-sequencing data for allelic expression.

Conclusions:

Accurate allelic expression analysis relies on rigorous error correction.
Quality control is essential for reliable interpretation of gene regulation and variation.
The provided tools facilitate high-throughput allelic expression studies.