Discover hidden splicing variations by mapping personal transcriptomes to personal genomes.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• RNA sequencing (RNA-seq) is crucial for studying alternative splicing variations.
• Current RNA-seq aligners struggle with genomic variants creating novel splice sites, leading to unmapped reads.
• This limitation hinders the comprehensive identification of splicing variations in personal transcriptomes.

Purpose of the Study:

• To develop and evaluate a novel approach for identifying 'hidden' splicing variations by aligning personal RNA-seq data to personal genomes.
• To discover previously undocumented splice junctions and their associations with human traits and diseases.

Main Methods:

• Developed a computational approach for mapping personal RNA-seq data to personal genomes.
• Utilized computational analysis and experimental validation to assess the accuracy of identified splice junctions.
• Applied the approach to RNA-seq data from 75 individuals.

Main Results:

• The developed approach successfully identified personal specific splice junctions with a low false positive rate.
• Discovered 506 personal specific splice junctions, including 437 novel junctions not present in current human transcript annotations.
• Identified 94 splice junctions with splice site SNPs associated with GWAS signals for human traits and diseases, including novel associations with diseases like ICA1.

Conclusions:

• The personal genome approach to RNA-seq alignment enables the discovery of a substantial catalog of previously unknown splicing variations in human populations.
• This method enhances the understanding of genetic variation in alternative splicing and its implications for human health.
• The findings highlight the importance of personalized genomics for comprehensive transcriptome analysis and disease association studies.