Estimation of isoform expression in RNA-seq data using a hierarchical bayesian model

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.

Now, researchers from Peking University have devloped a novel hierarchical Bayesian method to estimate isoform expression. While most of the existing methods treat gene expression as a by-product, they incorporate it into their 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.

Availability – The codes are available at

Wang Z, Wang J, Wu C, Deng M. (2015) Estimation of isoform expression in RNA-seq data using a hierarchical bayesian model. J Bioinform Comput Biol [Epub ahead of print]. [abstract]

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