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Predicting RNA secondary structures with pseudoknots by MCMC sampling

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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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Authors and Affiliations

  1. Institut für Informatik, J. W. Goethe-Universität, Robert Mayer Str. 11-15, 60325, Frankfurt am Main, Germany

    Dirk Metzler

  2. Fachbereich Informatik, Technische Universität Kaiserslautern, Gottlieb-Daimler-Str., 67663, Kaiserslautern, Germany

    Markus E. Nebel

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  1. Dirk Metzler
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Correspondence to Dirk Metzler.

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Metzler, D., Nebel, M.E. Predicting RNA secondary structures with pseudoknots by MCMC sampling. J. Math. Biol. 56, 161–181 (2008). https://doi.org/10.1007/s00285-007-0106-6

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  • DOI: https://doi.org/10.1007/s00285-007-0106-6

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