A massively parallel genetic algorithm for RNA secondary structure prediction
The Journal of Supercomputing
Pseudoknots in RNA secondary structures
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
A heuristic approach for detecting RNA H-type pseudoknots
Bioinformatics
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Structure clustering features on the Sfold Web server
Bioinformatics
RNA-DV: an interactive tool for editing and visualizing RNA secondary structures
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
RNA Secondary Structure Prediction Using Soft Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Ribonucleic acid (RNA), a single-stranded linear molecule, is essential to all biological systems. Different regions of the same RNA strand will fold together via base pair interactions to make intricate secondary and tertiary structures that guide crucial homeostatic processes in living organisms. Since the structure of RNA molecules is the key to their function, algorithms for the prediction of RNA structure are of great value. In this article, we demonstrate the usefulness of SARNA{\hbox{-}}Predict, an RNA secondary structure prediction algorithm based on Simulated Annealing (SA). A performance evaluation of SARNA{\hbox{-}}Predict in terms of prediction accuracy is made via comparison with eight state-of-the-art RNA prediction algorithms: mfold, Pseudoknot (pknotsRE), NUPACK, pknotsRG{\hbox{-}}mfe, Sfold, HotKnots, ILM, and STAR. These algorithms are from three different classes: heuristic,dynamic programming, and statistical sampling techniques. An evaluation for the performance of SARNA{\hbox{-}}Predict in terms of prediction accuracy was verified with native structures. Experiments on 33 individual known structures from eleven RNA classes (tRNA, viral RNA, antigenomic HDV, telomerase RNA, tmRNA, rRNA, RNaseP, 5S rRNA, Group I intron 23S rRNA, Group I intron 16S rRNA, and 16S rRNA) were performed. The results presented in this paper demonstrate that SARNA{\hbox{-}}Predict can out-perform other state-of-the-art algorithms in terms of prediction accuracy. Furthermore, there is substantial improvement of prediction accuracy by incorporating a more sophisticated thermodynamic model (efn2).