Article: A permutation based simulated annealing algorithm to predict pseudoknotted RNA secondary structures Journal: International Journal of Bioinformatics Research and Applications (IJBRA) 2015 Vol.11 No.5 pp.375 - 396 Abstract: Pseudoknots are RNA tertiary structures which perform essential biological functions. This paper discusses SARNA-Predict-pk, a RNA pseudoknotted secondary structure prediction algorithm based on Simulated Annealing (SA). The research presented here extends previous work of SARNA-Predict and further examines the effect of the new algorithm to include prediction of RNA secondary structure with pseudoknots. An evaluation of the performance of SARNA-Predict-pk in terms of prediction accuracy is made via comparison with several state-of-the-art prediction algorithms using 20 individual known structures from seven RNA classes. We measured the sensitivity and specificity of nine prediction algorithms. Three of these are dynamic programming algorithms: Pseudoknot (pknotsRE), NUPACK, and pknotsRG-mfe. One is using the statistical clustering approach: Sfold and the other five are heuristic algorithms: SARNA-Predict-pk, ILM, STAR, IPknot and HotKnots algorithms. The results presented in this paper demonstrate that SARNA-Predict-pk can out-perform other state-of-the-art algorithms in terms of prediction accuracy. This supports the use of the proposed method on pseudoknotted RNA secondary structure prediction of other known structures. Inderscience Publishers - linking academia, business and industry through research

Title: A permutation based simulated annealing algorithm to predict pseudoknotted RNA secondary structures

Authors: Herbert H. Tsang; Kay C. Wiese

Addresses: Applied Research Lab, Trinity Western University, Langley, British Columbia, Canada ' School of Computing Science, Simon Fraser University, Surrey, British Columbia, Canada

Abstract: Pseudoknots are RNA tertiary structures which perform essential biological functions. This paper discusses SARNA-Predict-pk, a RNA pseudoknotted secondary structure prediction algorithm based on Simulated Annealing (SA). The research presented here extends previous work of SARNA-Predict and further examines the effect of the new algorithm to include prediction of RNA secondary structure with pseudoknots. An evaluation of the performance of SARNA-Predict-pk in terms of prediction accuracy is made via comparison with several state-of-the-art prediction algorithms using 20 individual known structures from seven RNA classes. We measured the sensitivity and specificity of nine prediction algorithms. Three of these are dynamic programming algorithms: Pseudoknot (pknotsRE), NUPACK, and pknotsRG-mfe. One is using the statistical clustering approach: Sfold and the other five are heuristic algorithms: SARNA-Predict-pk, ILM, STAR, IPknot and HotKnots algorithms. The results presented in this paper demonstrate that SARNA-Predict-pk can out-perform other state-of-the-art algorithms in terms of prediction accuracy. This supports the use of the proposed method on pseudoknotted RNA secondary structure prediction of other known structures.

Keywords: RNA secondary structures; prediction accuracy; pseudoknots; RNA folding; ribonucleic acid; permutation; simulated annealing; bioinformatics.

DOI: 10.1504/IJBRA.2015.071938

International Journal of Bioinformatics Research and Applications, 2015 Vol.11 No.5, pp.375 - 396

Received: 11 Oct 2014
Accepted: 11 Feb 2015
Published online: 24 Sep 2015 *

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Title: A permutation based simulated annealing algorithm to predict pseudoknotted RNA secondary structures

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