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Bioinformatics. 2011 Sep 15;27(18):2554-62. doi: 10.1093/bioinformatics/btr444. Epub 2011 Jul 29.

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

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  • 1Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada.



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.


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.


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.




Supplementary data are available at Bioinformatics online.

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