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 
between-base dependence that affect read coverage profile throughout the 
transcript, it can lead to improved quantification of the true underlying 
expression level.
AVAILABILITY AND IMPLEMENTATION: POME can be freely downloaded at 
http://www.stat.purdue.edu/~yuzhu/pome.html.
CONTACT: yuzhu@purdue.edu; zhaohui.qin@emory.edu
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.