Tree adjoining grammars for RNA structure prediction
Theoretical Computer Science - Special issue: Genome informatics
Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots
Discrete Applied Mathematics - Special volume on combinatorial molecular biology
Classifying RNA pseudoknotted structures
Theoretical Computer Science
RNA pseudoknot prediction via an evolutionary algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Structural alignment of pseudoknotted RNA
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
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Accurate prediction of pseudoknotted RNA secondary structure is an important computational challenge. Typical prediction algorithms aim to find a structure with minimum free energy according to some thermodynamic (“sum of loop energies”) model that is implicit in the recurrences of the algorithm. However, a clear definition of what exactly are the loops and stems in pseudoknotted structures, and their associated energies, has been lacking. We present a comprehensive classification of loops in pseudoknotted RNA secondary structures. Building on an algorithm of Bader et al. [2] we obtain a linear time algorithm for parsing a secondary structures into its component loops. We also give a linear time algorithm to calculate the free energy of a pseudoknotted secondary structure. This is useful for heuristic prediction algorithms which are widely used since (pseudoknotted) RNA secondary structure prediction is NP-hard. Finally, we give a linear time algorithm to test whether a secondary structure is in the class handled by Akutsu's algorithm [1]. Using our tests, we analyze the generality of Akutsu's algorithm for real biological structures.