Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots
Discrete Applied Mathematics - Special volume on combinatorial molecular biology
A new approach to sequence comparison: normalized sequence alignment
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Edit distance between two RNA structures
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Computing similarity between RNA structures
Theoretical Computer Science
Classifying RNA pseudoknotted structures
Theoretical Computer Science
Consensus folding of unaligned RNA sequences revisited
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
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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.