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Tianjin, China

Reidys C.M.,Center for Combinatorics | Reidys C.M.,Nankai University | Huang F.W.D.,Center for Combinatorics | Andersen J.E.,University of Aarhus | And 9 more authors.

Motivation: Several dynamic programming algorithms for predicting RNA structures with pseudoknots have been proposed that differ dramatically from one another in the classes of structures considered. Results: Here, we use the natural topological classification of RNA structures in terms of irreducible components that are embeddable in the surfaces of fixed genus. We add to the conventional secondary structures four building blocks of genus one in order to construct certain structures of arbitrarily high genus. A corresponding unambiguous multiple context-free grammar provides an efficient dynamic programming approach for energy minimization, partition function and stochastic sampling. It admits a topology-dependent parametrization of pseudoknot penalties that increases the sensitivity and positive predictive value of predicted base pairs by 10-20% compared with earlier approaches. More general models based on building blocks of higher genus are also discussed. © The Author 2011. Published by Oxford University Press. All rights reserved. Source

Huang F.W.D.,Center for Combinatorics | Qin J.,Center for Combinatorics | Reidys C.M.,Center for Combinatorics | Reidys C.M.,Nankai University | And 6 more authors.

Motivation: It has been proven that the accessibility of the target sites has a critical influence on RNA-RNA binding, in general and the specificity and efficiency of miRNAs and siRNAs, in particular. Recently, O(N6) time and O(N4) space dynamic programming (DP) algorithms have become available that compute the partition function of RNA-RNA interaction complexes, thereby providing detailed insights into their thermodynamic properties. Results: Modifications to the grammars underlying earlier approaches enables the calculation of interaction probabilities for any given interval on the target RNA. The computation of the 'hybrid probabilities' is complemented by a stochastic sampling algorithm that produces a Boltzmann weighted ensemble of RNA-RNA interaction structures. The sampling of k structures requires only negligible additional memory resources and runs in O(k·N3). © The Author(s) 2009. Published by Oxford University Press. Source

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