title: Estimation of isoform expression in RNA-seq data using a hierarchical Bayesian model.
abstract: Estimation of gene or isoform expression is a fundamental step in many transcriptome analysis tasks, such as differential expression analysis, eQTL (or sQTL) studies, and biological network construction. RNA-seq technology enables us to monitor the expression on genome-wide scale at single base pair resolution and offers the possibility of accurately measuring expression at the level of isoform. However, challenges remain because of non-uniform read sampling and the presence of various biases in RNA-seq data. In this paper, we present a novel hierarchical Bayesian method to estimate isoform expression. While most of the existing methods treat gene expression as a by-product, we incorporate it into our model and explicitly describe its relationship with corresponding isoform expression using a Multinomial distribution. In this way, gene and isoform expression are included in a unified framework and it helps us achieve a better performance over other state-of-the-art algorithms for isoform expression estimation. The effectiveness of the proposed method is demonstrated using both simulated data with known ground truth and two real RNA-seq datasets from MAQC project. The codes are available at http://www.math.pku.edu.cn/teachers/dengmh/GIExp/.
authors.affiliation: * Center for Quantitative Biology, Peking University, Beijing 100871, P. R. China.
authors.forename: Zengmiao
authors.initials: Z
authors.lastname: Wang
authors.affiliation: * Center for Quantitative Biology, Peking University, Beijing 100871, P. R. China.
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
authors.affiliation: † School of Mathematical Sciences, Peking University, Beijing 100871, P. R. China.
authors.forename: Changjing
authors.initials: C
authors.lastname: Wu
authors.affiliation: * Center for Quantitative Biology, Peking University, Beijing 100871, P. R. China.|‡ LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, P. R. China.|§ Center for Statistical Science, Peking University, Beijing 100871, P. R. China.
authors.forename: Minghua
authors.initials: M
authors.lastname: Deng
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology; D030541:Databases, Genetic; D015870:Gene Expression; D006801:Humans; D008957:Models, Genetic; D009010:Monte Carlo Method; D020033:Protein Isoforms / Q000235:genetics*; D011565:Psoriasis / Q000235:genetics; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D020033:Protein Isoforms
keywords: RNA-seq; expression; gene; hierarchical bayesian model; isoform