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

SUBMITTER: Hu M 

PROVIDER: S-EPMC3244770 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature