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Biol."],"published-print":{"date-parts":[[2015,12]]},"abstract":"<jats:p> 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.\u00a0RNA-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.\u00a0However, challenges remain because of non-uniform read sampling and the presence of various biases in RNA-seq data.\u00a0In this paper, we present a novel hierarchical Bayesian method to estimate isoform expression.\u00a0While 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.\u00a0In 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.\u00a0The effectiveness of the proposed method is demonstrated using both simulated data with known ground truth and two real RNA-seq datasets from MAQC project. 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