Finding a Common Motif of RNA Sequences Using Genetic Programming: The GeRNAMo System
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
RnaPredict—An Evolutionary Algorithm for RNA Secondary Structure Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Predicting RNA secondary structure based on the class information and Hopfield network
Computers in Biology and Medicine
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
RNA pseudoknot prediction via an evolutionary algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Approximation Algorithms for Predicting RNA Secondary Structures with Arbitrary Pseudoknots
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
RNA-DV: an interactive tool for editing and visualizing RNA secondary structures
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
RNA Secondary Structure Prediction Using Soft Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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@cs.sfu.ca). Contact: wiese@cs.sfu.ca