A Bayesian approach for estimating allele-specific expression from RNA-Seq data with diploid genomes

摘要

Background: RNA-sequencing (RNA-Seq) has become a popular tool fortranscriptome profiling in mammals. However, accurate estimation of allele-specific expression (ASE) based on alignments of reads to the reference genome is challenging, because it contains only one allele on a mosaic haploid genome. Even with the information of diploid genome sequences, precise alignment of reads to the correct allele is difficult because of the high-similarity between the corresponding allele sequences.Results: We propose a Bayesian approach to estimate ASE from RNA-Seq data with diploid genome sequences. In th e statistical framework, the haploid choice is modeled as a hidden variable and estimated simultaneously with isoform e xpression levels by variational Bayesian inference. Through the simulation data analysis, we demonstrate the effectivenes s of the proposed approach in terms of identifying ASE compared to the existing approach. We also show that our approach enables better quantification of isoform expression levels compared to the existing methods, TIGAR2, RSEM and Cufflinks. In the real data analysis of the human reference lymphoblastoid cell line GM12878, some autosomal genes were identified as ASE genes, and skewed paternal X-chromosomeinactivation in GM12878 was identified.Conclusions: The proposed method, called ASE-TIGAR, enables accurate estimation of gene expression from RNA-Seq data in an allele-specific manner. Our results show the effectiveness of utilizing personal genomic information for accurate estimation of ASE. An implementation of our method is available at http://nagasakilab.csml.org/ase-tigar.