An MCMC-Method for Sampling RNA Secondary Structures with Pseudoknots

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


Markov-Chain Monte-Carlo, Stochastic Context-Free Grammar, RNA Structure Prediction, Pseudoknots, Stochastic Modeling.


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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