Using poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq

Abstract

Motivation: rna sequencing (rna-seq) is a powerful new technology for mapping and quantifying transcriptomes using ultra high-throughput next-generation sequencing technologies. using deep sequencing, gene expression levels of all transcripts including novel ones can be quantified digitally. although extremely promising, the massive amounts of data generated by rna-seq, substantial biases and uncertainty in short read alignment pose challenges for data analysis. in particular, large base-specific variation and between-base dependence make simple approaches, such as those that use averaging to normalize rna-seq data and quantify gene expressions, ineffective. Results: In this study, we propose a Poisson mixed-effects (POME) model to characterize base-level read coverage within each transcript. The underlying expression level is included as a key parameter in this model. Since the proposed model is capable of incorporating base-specific variation as well as betweenbase dependence that affect read coverage profile throughout the transcript, it can lead to improved quantification of the true underlying expression level. © The Author 2011. Published by Oxford University Press. All rights reserved.