Computational prediction of nucleic acid secondary structure: Methods, applications, and challenges
Theoretical Computer Science
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Prediction of RNA secondary structure including kissing hairpin motifs
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
Sparse RNA folding: Time and space efficient algorithms
Journal of Discrete Algorithms
Rich parameterization improves RNA structure prediction
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Designing RNA secondary structures in coding regions
ISBRA'12 Proceedings of the 8th international conference on Bioinformatics Research and Applications
An information security-based literature survey and classification framework of data storage in DNA
International Journal of Networking and Virtual Organisations
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Motivation: Accurate prediction of RNA secondary structure from the base sequence is an unsolved computational challenge. The accuracy of predictions made by free energy minimization is limited by the quality of the energy parameters in the underlying free energy model. The most widely used model, the Turner99 model, has hundreds of parameters, and so a robust parameter estimation scheme should efficiently handle large data sets with thousands of structures. Moreover, the estimation scheme should also be trained using available experimental free energy data in addition to structural data. Results: In this work, we present constraint generation (CG), the first computational approach to RNA free energy parameter estimation that can be efficiently trained on large sets of structural as well as thermodynamic data. Our CG approach employs a novel iterative scheme, whereby the energy values are first computed as the solution to a constrained optimization problem. Then the newly computed energy parameters are used to update the constraints on the optimization function, so as to better optimize the energy parameters in the next iteration. Using our method on biologically sound data, we obtain revised parameters for the Turner99 energy model. We show that by using our new parameters, we obtain significant improvements in prediction accuracy over current state of-the-art methods. Availability: Our CG implementation is available at http://www.rnasoft.ca/CG/ Contact: andrones@cs.ubc.ca