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Bioinformatics Advance Access originally published online on February 10, 2006
Bioinformatics 2006 22(8):934-942; 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
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

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

Kay C. Wiese * and Andrew Hendriks

School of Computing Science and InfoNet Media Centre, Simon Fraser University 15th Floor, Central City Tower, 13450 102nd Avenue, Surrey, BC, Canada V3T 5X3

*To whom correspondence should be addressed.

Motivation: Ribonucleic acid is vital in numerous stages of protein synthesis; it also possesses important functional and structural roles within the cell. The function of an 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 by the 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 for RNA secondary structure prediction. The speedup provided by parallelization is investigated with five 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 with 10 individual known structures from 3 RNA classes (5S rRNA, Group I intron 16S rRNA and 16S rRNA) and the mfold dynamic programming algorithm. 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{at}cs.sfu.ca).

Contact: wiese{at}cs.sfu.ca

Received on November 4, 2005; revised on February 2, 2006; accepted on February 3, 2006

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