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Parametrized Stochastic Grammars for RNA Secondary Structure Prediction

Robert S. Maier
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Abstract

We propose a two-level stochastic context-free grammar (SCFG) architecture for parametrized stochastic modeling of a family of RNA sequences, including their secondary structure. A stochastic model of this type can be used for maximum a posteriori estimation of the secondary structure of any new sequence in the family. The proposed SCFG architecture models RNA subsequences comprising paired bases as stochastically weighted Dyck-language words, i.e., as weighted balanced-parenthesis expressions. The length of each run of unpaired bases, forming a loop or a bulge, is taken to have a phase-type distribution: that of the hitting time in a finite-state Markov chain. Without loss of generality, each such Markov chain can be taken to have a bounded complexity. The scheme yields an overall family SCFG with a manageable number of parameters.

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BibTeXRIS

Robert S. Maier. 2007-01-24. Parametrized Stochastic Grammars for RNA Secondary Structure Prediction. https://doi.org/10.1109/ita.2007.4357589

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