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Bioinformatics Advance Access published online on February 10, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl043
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received November 4, 2005
Revised February 2, 2006
Accepted February 3, 2006

Article

Comparison of P-RnaPredict and mfold - algorithms for RNA secondary structure prediction

Kay C. Wiese 1 * and Andrew Hendriks 1

1 School of Computing Science and InfoNet Media Centre, Simon Fraser University, 15th Floor, Central City Tower, 13450 102nd Ave., Surrey, B.C., Canada, V3T 5X3

* To whom correspondence should be addressed.
Kay C. Wiese, E-mail: wiese{at}cs.sfu.ca


  Abstract

Motivation: Ribonucleic acid (RNA) is vital in numerous stages of protein synthesis; it also possesses important functional and structural roles within the cell. The function of a RNA molecule within a particular organic system is principally determined by its structure. The current physical methods available for structure determination are time consuming and expensive. Hence, computational methods for structure prediction are sought after. The energies involved through formation of secondary structure elements are significantly greater than those of tertiary elements. Therefore, RNA structure prediction focuses on secondary structure.

Results: We present P-RnaPredict, a parallel evolutionary algorithm (EA) for RNA secondary structure prediction. The speedup provided by parallelization is investigated with 5 sequences, and a dramatic improvement in speedup is demonstrated, especially with longer sequences. An evaluation of the performance of P-RnaPredict in terms of prediction accuracy is made through comparison to 10 individual known structures from 3 RNA classes (5S rRNA, Group I intron 16S rRNA, and 16S rRNA) and the mfold (Zuker, 2003) dynamic programming algorithm (DPA). P-RnaPredict is able to predict structures with higher true positive base pair counts and lower false positives than mfold on certain sequences.

Availability: P-RnaPredict is available for non-commercial usage. Interested parties should contact Kay C. Wiese (wiese@cs.sfu.ca).

Associate Editor: Anna Tramontano
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