A non-parametric Bayesian approach for predicting RNA secondary structures

Authors:
Kengo Sato;Michiaki Hamada;Toutai Mituyama;Kiyoshi Asai;Yasubumi Sakakibara
Affiliations:
Japan Biological Inf. Consortium, Tokyo, Japan and Computational Biology Research Center, National Institute of Advanced Industrial Science and Techn., Tokyo, Japan and Dept. of Biosciences and In ...;Mizuho Information & Research Institute, Inc, Tokyo, Japan and Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan;Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan;Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Japan and Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan;Department of Biosciences and Informatics, Keio University, Yokohama, Kanagawa, Japan and Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, T ...
Venue:
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
Year:
2009

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Abstract

Since many functional RNAs form stable secondary structures which are related to their functions, RNA secondary structure prediction is a crucial problem in bioinformatics. We propose a novel model for generating RNA secondary structures based on a non-parametric Bayesian approach, called hierarchical Dirichlet processes for stochastic context-free grammars (HDP-SCFGs). Here non-parametric means that some meta-parameters, such as the number of non-terminal symbols and production rules, do not have to be fixed. Instead their distributions are inferred in order to be adapted (in the Bayesian sense) to the training sequences provided. The results of our RNA secondary structure predictions show that HDP-SCFGs are more accurate than the MFE-based and other generative models.