RNA Secondary Structure Prediction Based on Forest Representation and Genetic Algorithm

RNA secondary structure prediction is one of the most important research areas in bioinformatics. This paper presents an improved genetic algorithm to predict RNA secondary structure. Firstly, we introduce the forest representation of RNA secondary structure. Next, we present a permutation-based genetic algorithm for predicting RNA secondary structures. We compute all possible helices of the RNA molecule and select the helices which have the highest similarity with the helices of a given homologous molecular structure. Finally, we select the structure which has the highest structure stability and similarity as predicted structure. Experiments have proved that the precise rates are improved.

[1]  Julien Allali,et al.  A New Distance for High Level RNA Secondary Structure Comparison , 2005, TCBB.

[2]  Robert Giegerich,et al.  Local similarity in RNA secondary structures , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.

[3]  Rolf Backofen,et al.  Backofen R: MARNA: multiple alignment and consensus structure prediction of RNAs based on sequence structure comparisons , 2005 .

[4]  Scott D. Goodwin,et al.  Keep–Best Reproduction: A Local Family Competition Selection Strategy and the Environment it Flourishes in , 2001, Constraints.

[5]  D. Turner,et al.  Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Pierre Baldi,et al.  Assessing the accuracy of prediction algorithms for classification: an overview , 2000, Bioinform..

[7]  Bjarne Knudsen,et al.  RNA secondary structure prediction using stochastic context-free grammars and evolutionary history , 1999, Bioinform..

[8]  Hélène Touzet,et al.  Finding the common structure shared by two homologous RNAs , 2003, Bioinform..

[9]  Qi Liu,et al.  A Hopfield Neural Network Based Algorithm for RNA Secondary Structure Prediction , 2006, First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06).

[10]  K. Wiese,et al.  A permutation-based genetic algorithm for the RNA folding problem: a critical look at selection strategies, crossover operators, and representation issues. , 2003, Bio Systems.

[11]  J. Sabina,et al.  Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. , 1999, Journal of molecular biology.

[12]  Robert Giegerich,et al.  A comprehensive comparison of comparative RNA structure prediction approaches , 2004, BMC Bioinformatics.

[13]  Maozu Guo,et al.  A Permutation-Based Genetic Algorithm for Predicting RNA Secondary Structure-A Practicable Approach , 2005, FSKD.