An MCMC-Method for Sampling RNA Secondary Structures with Pseudoknots

D. Metzler and M.E. Nebel (Germany)

Keywords

MarkovChain MonteCarlo, Stochastic ContextFree Grammar, RNA Structure Prediction, Pseudoknots, Stochastic Modeling.

Abstract

The most probable secondary structure of an RNA molecule, given the nucleotide sequence, can be computed efficiently if a stochastic context-free grammar (SCFG) is used as the prior distribution of the secondary structure. The structures of some RNA molecules contain so-called pseudoknots. Allowing all possible configurations of pseu doknots is not compatible with context-free grammar mod els and makes the search for an optimal secondary structure NP-complete. We suggest a probabilistic model for RNA secondary structures with pseudoknots and present a Markov-chain Monte-Carlo Method for sampling RNA structures accord ing to their posterior distribution for a given sequence. We demonstrate the benefit of our method by applying it to tm RNA.

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