Efficient sampling of RNA secondary structures from the Boltzmann ensemble of low-energy: The boustrophedon method

Yann Ponty 1, 2
1 AMIB - Algorithms and Models for Integrative Biology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France
Abstract : We adapt here a surprising technique, the boustrophedon method, to speed up the sampling of RNA secondary structures from the Boltzmann low-energy ensemble. This technique is simple and its implementation straight-forward, as it only requires a permutation in the order of some operations already performed in the stochastic traceback stage of these algorithms. It nevertheless greatly improves their worst-case complexity from O(n^2) to O(n log(n)), for n the size of the original sequence. Moreover the average-case complexity of the generation is shown to be improved from O(n√n) to O(n log(n)) in an Boltzmann-weighted homopolymer model based on the Nussinov–Jacobson free-energy model. These results are extended to the more realistic Turner free-energy model through experiments performed on both structured (Drosophilia melanogaster mRNA 5S) and hybrid (Staphylococcus aureus RNAIII) RNA sequences, using a boustrophedon modified version of the popular software UnaFold. This improvement allows for the sampling of greater and more significant sets of structures in a given time.
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Contributor : Yann Ponty <>
Submitted on : Monday, December 20, 2010 - 4:44:16 PM
Last modification on : Wednesday, March 27, 2019 - 4:41:29 PM

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Yann Ponty. Efficient sampling of RNA secondary structures from the Boltzmann ensemble of low-energy: The boustrophedon method. Journal of Mathematical Biology, Springer Verlag (Germany), 2008, 56 (1-2), pp.107--127. ⟨10.1007/s00285-007-0137-z⟩. ⟨inria-00548863⟩

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