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	Estimation of isoform expression in RNA-seq data using a hierarchical Bayesian model
	Wang, Zengmiao ; Wang, Jun ; Wu, Changjing ; Deng, Minghua
	2015
关键词	RNA-seq isoform gene expression hierarchical bayesian model GENE-EXPRESSION DIFFERENTIAL EXPRESSION ACCURATE QUANTIFICATION TRANSCRIPTOME PROVIDES TOOL
英文摘要	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.; National Natural Science Foundation of China [31171262, 31428012, 31471246]; National Key Basic Research Project of China [2015CB910303]; SCI(E); PubMed; ARTICLE; wangzengmiao@pku.edu.cn; junwangcqb@pku.edu.cn; wcj@pku.edu.cn; dengmh@pku.edu.cn; 6,SI; 1542001; 13
语种	英语
出处	PubMed ; SCI
出版者	JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/417995]
专题	数学科学学院 生命科学学院
推荐引用方式 GB/T 7714	Wang, Zengmiao,Wang, Jun,Wu, Changjing,et al. Estimation of isoform expression in RNA-seq data using a hierarchical Bayesian model. 2015-01-01.

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