Read-mapping using personalized diploid reference genome for RNA sequencing data reduced bias for detecting...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Next-generation sequencing (NGS) is widely used in genetics and genomics.
• Mapping short sequencing reads to a reference genome is a fundamental challenge.
• Existing methods often use a single reference genome, ignoring individual genetic variants, leading to mapping inaccuracies and biased results, such as in allele-specific expression analysis.

Purpose of the Study:

• To develop a method for creating a personalized diploid reference genome.
• To leverage graphics processing unit (GPU) parallel computing for this task.
• To improve the accuracy of mapping sequencing reads and reduce bias in downstream analyses.

Main Methods:

• Utilized DirectX 11 enabled graphics processing unit (GPU) parallel computing power.
• Developed a method to construct a personalized diploid reference genome incorporating known genetic variants.
• Applied the personalized reference genome to read mapping without altering existing alignment algorithms.

Main Results:

• The personalized diploid reference genome significantly improved read mapping accuracy.
• The method substantially reduced the bias toward the reference allele in allele-specific expression analysis.
• The approach is applicable to individuals with genotype information from various sources.

Conclusions:

• Personalized diploid reference genomes enhance the accuracy of NGS data analysis.
• GPU-accelerated methods offer an efficient solution for constructing personalized references.
• This approach provides a valuable tool for precise genetic variant interpretation from NGS data.