A haplotype-based normalization technique for the analysis and detection of allele specific expression

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Allele-specific expression (ASE) is a crucial phenotype for understanding genetic variation and disease.
• Current methods for detecting ASE from high-throughput sequencing data face challenges with mapping biases and computational demands.
• There is a need for efficient and precise methods to analyze the growing volume of sequencing data.

Purpose of the Study:

• To present a new, fast, and accurate method for normalizing RNA sequencing data to detect allele-specific expression (ASE) events.
• To improve the quantification of reference allele expression at heterozygous sites.

Main Methods:

• Developed a novel RNA sequencing data normalization approach.
• Utilized simulated datasets to evaluate the method's performance.
• Applied the method to exome and transcriptome data from 96 individuals in the CARTaGENE cohort.

Main Results:

• The new normalization approach significantly improves reference allele quantification at heterozygous sites compared to default mapping.
• The method performs competitively with existing techniques like filtering and parental genome mapping, without complex manipulation.
• A significant association was found between the proportion of sites with ASE and smoking status in the studied cohort.

Conclusions:

• Proper normalization of RNA sequencing data is essential for controlling mapping biases and identifying true ASE signals.
• The developed normalization method effectively identifies biologically relevant signals in personal genomes.
• This approach facilitates the analysis of ASE in large-scale genomic projects.