Bayesian prediction of tissue-regulated splicing using RNA sequence and cellular context

被引:62
作者
Xiong, Hui Yuan [1 ]
Barash, Yoseph [1 ,2 ]
Frey, Brendan J. [1 ,2 ]
机构
[1] Univ Toronto, Dept Elect & Comp Engn, Toronto, ON M5G 3G4, Canada
[2] Univ Toronto, Ctr Cellular & Biomol Res, Banting & Best Dept Med Res, Toronto, ON M5G 3E1, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
INSIGHTS;
D O I
10.1093/bioinformatics/btr444
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: Alternative splicing is a major contributor to cellular diversity in mammalian tissues and relates to many human diseases. An important goal in understanding this phenomenon is to infer a 'splicing code' that predicts how splicing is regulated in different cell types by features derived from RNA, DNA and epigenetic modifiers. Methods: We formulate the assembly of a splicing code as a problem of statistical inference and introduce a Bayesian method that uses an adaptively selected number of hidden variables to combine subgroups of features into a network, allows different tissues to share feature subgroups and uses a Gibbs sampler to hedge predictions and ascertain the statistical significance of identified features. Results: Using data for 3665 cassette exons, 1014 RNA features and 4 tissue types derived from 27 mouse tissues (http://genes.toronto.edu/wasp), we benchmarked several methods. Our method outperforms all others, and achieves relative improvements of 52% in splicing code quality and up to 22% in classification error, compared with the state of the art. Novel combinations of regulatory features and novel combinations of tissues that share feature subgroups were identified using our method.
引用
收藏
页码:2554 / 2562
页数:9
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