RNA-RNA interaction prediction using genetic algorithm

Algorithms for Molecular Biology

Soheila Montaseri²  Fatemeh Zare-Mirakabad³  Nasrollah Moghadam-Charkari¹ 

[1] Faculty of Electrical & Computer Engineering, Tarbiat Modares University, Tehran, Iran

[2] Department of Mathematics, Statistics and Computer Sciences, University of Tehran, Tehran, Iran

[3] School of Biological Science, Institute for Research in Fundamental Sciences (IPM), P.O. Box: 19395- 5746, Tehran, Iran

关键词: Fitness function;    Minimum free energy;    RNA secondary structure;   

Others  :  1082126
DOI  :  10.1186/1748-7188-9-17

 received in 2013-01-19, accepted in 2014-06-18,  发布年份 2014

【 摘 要 】

Background

RNA-RNA interaction plays an important role in the regulation of gene expression and cell development. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. In the RNA-RNA interaction prediction problem, two RNA sequences are given as inputs and the goal is to find the optimal secondary structure of two RNAs and between them. Some different algorithms have been proposed to predict RNA-RNA interaction structure. However, most of them suffer from high computational time.

Results

In this paper, we introduce a novel genetic algorithm called GRNAs to predict the RNA-RNA interaction. The proposed algorithm is performed on some standard datasets with appropriate accuracy and lower time complexity in comparison to the other state-of-the-art algorithms. In the proposed algorithm, each individual is a secondary structure of two interacting RNAs. The minimum free energy is considered as a fitness function for each individual. In each generation, the algorithm is converged to find the optimal secondary structure (minimum free energy structure) of two interacting RNAs by using crossover and mutation operations.

Conclusions

This algorithm is properly employed for joint secondary structure prediction. The results achieved on a set of known interacting RNA pairs are compared with the other related algorithms and the effectiveness and validity of the proposed algorithm have been demonstrated. It has been shown that time complexity of the algorithm in each iteration is as efficient as the other approaches.

【 授权许可】

   
2014 Montaseri et al.; licensee BioMed Central Ltd.

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