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


BACKGROUND
P-RnaPredict is available for non-commercial usage. Interested parties should contact Kay C. Wiese (wiese@cs.sfu.ca).
School of Computing Science and InfoNet Media Centre, Simon Fraser University, 15th Floor, Central City Tower, 13450 102nd Avenue, Surrey, BC, Canada V3T 5X3. wiese@cs.sfu.ca
Kay C
KC
Wiese
Andrew
A
Hendriks
D000465:Algorithms; D001483:Base Sequence; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009690:Nucleic Acid Conformation; D016415:Sequence Alignment / Q000379:methods; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software