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

Hu M, Zhu Y, Taylor JM, Liu JS, Qin ZS
Bioinformatics. 2012 28 (1): 63-8

PMID: 22072384 · PMCID: PMC3244770 · DOI:10.1093/bioinformatics/btr616

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.

MeSH Terms (10)

Cell Line, Tumor Gene Expression Profiling High-Throughput Nucleotide Sequencing Humans Male Microarray Analysis Models, Statistical Prostatic Neoplasms Sequence Analysis, RNA Transcriptome

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