ASAS-EGB: A statistical framework for estimating allele-specific alternative splicing events using transcriptome data

Abstract

Ubiquitous Alternative splicing (AS) in eukaryotes greatly increases the biodiversity of proteins and involves in disease and cancer. Allele-specific AS studies can facilitate the identification of cis-acting elements because both alleles share the same cellular environment. Due to the limited information provided on the exons defined by AS events, we propose a statistical framework and algorithm ASAS-EGB for ASAS analysis using the gene transcriptome. The framework obtains exclusively compatible sets of gene isoforms supporting each event isoform, and uses both phased and non-phased SNPs within the exons on the gene isoforms for inference. Using this strategy, we have demonstrated ASAS-EGB can yield better ASAS inferential performance than using event isoforms. ASAS-EGB supports both single-end and paired-end RNA-seq data, and we have proved its robustness using RNA-seq replicates of individual NA12878. ASAS-EGB builds Bayesian models for ASAS analysis, and the MCMC method is used to solve the problem. With more detailed annotations for individual genomes and transcriptomes appearing in the future, the algorithm proposed by the paper can provide better support for these data to reveal the regulatory mechanisms of individual genomes.