RNA Secondary Structure Prediction Via Energy Density Minimization

  • Can Alkan
  • Emre Karakoc
  • S. Cenk Sahinalp
  • Peter Unrau
  • H. Alexander Ebhardt
  • Kaizhong Zhang
  • Jeremy Buhler
Conference paper

DOI: 10.1007/11732990_12

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3909)
Cite this paper as:
Alkan C. et al. (2006) RNA Secondary Structure Prediction Via Energy Density Minimization. In: Apostolico A., Guerra C., Istrail S., Pevzner P.A., Waterman M. (eds) Research in Computational Molecular Biology. RECOMB 2006. Lecture Notes in Computer Science, vol 3909. Springer, Berlin, Heidelberg

Abstract

There is a resurgence of interest in RNA secondary structure prediction problem (a.k.a. the RNA folding problem) due to the discovery of many new families of non-coding RNAs with a variety of functions. The vast majority of the computational tools for RNA secondary structure prediction are based on free energy minimization. Here the goal is to compute a non-conflicting collection of structural elements such as hairpins, bulges and loops, whose total free energy is as small as possible. Perhaps the most commonly used tool for structure prediction, mfold/RNAfold, is designed to fold a single RNA sequence. More recent methods, such as RNAscf and alifold are developed to improve the prediction quality of this tool by aiming to minimize the free energy of a number of functionally similar RNA sequences simultaneously. Typically, the (stack) prediction quality of the latter approach improves as the number of sequences to be folded and/or the similarity between the sequences increase. If the number of available RNA sequences to be folded is small then the predictive power of multiple sequence folding methods can deteriorate to that of the single sequence folding methods or worse.

In this paper we show that delocalizing the thermodynamic cost of forming an RNA substructure by considering the energy density of the substructure can significantly improve on secondary structure prediction via free energy minimization. We describe a new algorithm and a software tool that we call Densityfold, which aims to predict the secondary structure of an RNA sequence by minimizing the sum of energy densities of individual substructures. We show that when only one or a small number of input sequences are available, Densityfold can outperform all available alternatives. It is our hope that this approach will help to better understand the process of nucleation that leads to the formation of biologically relevant RNA substructures.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Can Alkan
    • 1
  • Emre Karakoc
    • 2
  • S. Cenk Sahinalp
    • 2
  • Peter Unrau
    • 3
  • H. Alexander Ebhardt
    • 3
  • Kaizhong Zhang
    • 4
  • Jeremy Buhler
    • 5
  1. 1.Department of Genome SciencesUniversity of WashingtonUSA
  2. 2.School of Computing ScienceSimon Fraser UniversityCanada
  3. 3.Department of Molecular Biology and BiochemistrySimon Fraser UniversityCanada
  4. 4.Department of Computer ScienceUniversity of Western OntarioCanada
  5. 5.Department of Computer ScienceWashington University in St LouisUSA

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