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Int J Bioinform Res Appl. 2015;11(5):375-96.

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

Author information

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.

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

PMID:
26558299

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