Predicting RNA secondary structures with pseudoknots by MCMC sampling |
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Authors: | Dirk Metzler Markus E Nebel |
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Institution: | 1. Institut für Informatik, J. W. Goethe-Universit?t, Robert Mayer Str. 11-15, 60325, Frankfurt am Main, Germany 2. Fachbereich Informatik, Technische Universit?t Kaiserslautern, Gottlieb-Daimler-Str., 67663, Kaiserslautern, Germany
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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 pseudoknots is not compatible with context-free
grammar models 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 according
to their posterior distribution for a given sequence. We favor Bayesian sampling over optimization methods in this context,
because it makes the uncertainty of RNA structure predictions assessable. We demonstrate the benefit of our method in examples
with tmRNA and also with simulated data. McQFold, an implementation of our method, is freely available from http://www.cs.uni-frankfurt.de/~metzler/McQFold. |
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Keywords: | 65C05 62P10 92B05 62M09 |
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