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Tsang HH, Wiese KC. A permutation based simulated annealing algorithm to predict pseudoknotted RNA secondary structures. Int J Bioinform Res Appl. 2015;11(5):375-96doi: 10.1504/ijbra.2015.071938.
Tsang, H. H., & Wiese, K. C. (2015). A permutation based simulated annealing algorithm to predict pseudoknotted RNA secondary structures. International journal of bioinformatics research and applications, 11(5), 375-96. https://doi.org/10.1504/ijbra.2015.071938
Tsang, Herbert H, and Wiese, Kay C. "A permutation based simulated annealing algorithm to predict pseudoknotted RNA secondary structures." International journal of bioinformatics research and applications vol. 11,5 (2015): 375-96. doi: https://doi.org/10.1504/ijbra.2015.071938
Tsang HH, Wiese KC. A permutation based simulated annealing algorithm to predict pseudoknotted RNA secondary structures. Int J Bioinform Res Appl. 2015;11(5):375-96. doi: 10.1504/ijbra.2015.071938. PMID: 26558299.
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Int J Bioinform Res Appl. 2015;11(5):375-96. doi: 10.1504/ijbra.2015.071938.

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

International journal of bioinformatics research and applications

Herbert H Tsang, Kay C Wiese

Affiliations

  1. 1 Applied Research Lab, Trinity Western University, Langley, British Columbia, Canada.
  2. 2 School of Computing Science, Simon Fraser University, Surrey, British Columbia, Canada.

PMID: 26558299 DOI: 10.1504/ijbra.2015.071938

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

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