K-Partite RNA Secondary Structures

Jiang, Minghui; Tejada, Pedro J.; Lasisi, Ramoni O.; Cheng, Shanhong; Fechser, D. Scott

doi:10.1089/cmb.2009.0119

Cited by 2 publications

(2 citation statements)

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“…The maximum complexity of a secondary structure predicted by is restricted by the number m of decomposed levels of pseudoknots, which is also called an m -partite RNA secondary structure (Jiang et al , 2010), defined as the union of m pseudoknot-free substructures. A recent study has implied that most known RNA secondary structures are either bipartite or tripartite, i.e.…”

Section: Discussionmentioning

confidence: 99%

IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming

et al. 2011

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Motivation: Pseudoknots found in secondary structures of a number of functional RNAs play various roles in biological processes. Recent methods for predicting RNA secondary structures cover certain classes of pseudoknotted structures, but only a few of them achieve satisfying predictions in terms of both speed and accuracy.Results: We propose , a novel computational method for predicting RNA secondary structures with pseudoknots based on maximizing expected accuracy of a predicted structure. decomposes a pseudoknotted structure into a set of pseudoknot-free substructures and approximates a base-pairing probability distribution that considers pseudoknots, leading to the capability of modeling a wide class of pseudoknots and running quite fast. In addition, we propose a heuristic algorithm for refining base-paring probabilities to improve the prediction accuracy of . The problem of maximizing expected accuracy is solved by using integer programming with threshold cut. We also extend so that it can predict the consensus secondary structure with pseudoknots when a multiple sequence alignment is given. is validated through extensive experiments on various datasets, showing that achieves better prediction accuracy and faster running time as compared with several competitive prediction methods.Availability: The program of is available at http://www.ncrna.org/software/ipknot/. is also available as a web server at http://rna.naist.jp/ipknot/.Contact: satoken@k.u-tokyo.ac.jp; ykato@is.naist.jpSupplementary information: Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming

et al. 2011

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“…One of the classification approaches is k-noncrossing matching [17], [18], where a k-noncrossing structure has no more than k –1 crossings of its base pair arcs (each arc representing a stem). Another approach is called k-partite, meaning that an RNA secondary structure can be divided into k substructures which are pseudoknot-free [19]. We are interested in another classification that applies quantum matrix field theory [20].…”

Section: Introductionmentioning

confidence: 99%

Conformational Features of Topologically Classified RNA Secondary Structures

Chiu

Chen

2012

PLoS ONE

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BackgroundCurrent RNA secondary structure prediction approaches predict prevalent pseudoknots such as the H-pseudoknot and kissing hairpin. The number of possible structures increases drastically when more complex pseudoknots are considered, thus leading to computational limitations. On the other hand, the enormous population of possible structures means not all of them appear in real RNA molecules. Therefore, it is of interest to understand how many of them really exist and the reasons for their preferred existence over the others, as any new findings revealed by this study might enhance the capability of future structure prediction algorithms for more accurate prediction of complex pseudoknots.Methodology/Principal FindingsA novel algorithm was devised to estimate the exact number of structural possibilities for a pseudoknot constructed with a specified number of base pair stems. Then, topological classification was applied to classify RNA pseudoknotted structures from data in the RNA STRAND database. By showing the vast possibilities and the real population, it is clear that most of these plausible complex pseudoknots are not observed. Moreover, from these classified motifs that exist in nature, some features were identified for further investigation. It was found that some features are related to helical stacking. Other features are still left open to discover underlying tertiary interactions.ConclusionsResults from topological classification suggest that complex pseudoknots are usually some well-known motifs that are themselves complex or the interaction results of some special motifs. Heuristics can be proposed to predict the essential parts of these complex motifs, even if the required thermodynamic parameters are currently unknown.

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K-Partite RNA Secondary Structures

Cited by 2 publications

References 33 publications

IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming

IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming

Conformational Features of Topologically Classified RNA Secondary Structures

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