Journal of Mathematical Biology

, Volume 56, Issue 1–2, pp 107–127 | Cite as

Efficient sampling of RNA secondary structures from the Boltzmann ensemble of low-energy

The boustrophedon method
  • Yann PontyEmail author


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 \({\mathcal{O}}({n^2})\) to \({\mathcal{O}}({n\log(n)})\) , for n the size of the original sequence. Moreover the average-case complexity of the generation is shown to be improved from \({\mathcal{O}}({n\sqrt{n}})\) to \({\mathcal{O}}({n\log(n)})\) 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.


Statistical sampling Boltzmann free-energy ensemble RNA structure MFE folding 

Mathematics Subject Classification (2000)

92E10 92C40 05A16 68Q25 82B41 


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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Biology Department, Higgins Hall 577Boston CollegeChestnut HillUSA

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