RNA secondary structure prediction based on genetic algorithm and comparative approach

Ribonucleic acid (RNA) molecule is a vital research field in bioinformatics, especially at the level of determining their functions and structures. RNA structure prediction considered among the most challenging computational problems, and a central task in bioinformatics. Despite the various approaches that have been proposed to deal with this task, the area of improvement of the prediction is always open in terms of structure accuracy and the complexities of both time and space. In this paper, we present a new method to predict RNA secondary structure. The proposed method combines between two main approaches, the comparative and the thermodynamic approaches. As input three elements should be given: 1) a sequence for which the secondary structure is searched. 2) a sequence with a known secondary structure. We should note that these two sequences must be homologous. 3) The secondary structure of the second sequence. To evaluate the performance of the proposed method a comparative study has been performed between our method and some of the most known RNA secondary structure algorithms. The results obtained show that the proposed algorithm can improve the performance of the predicted structure compared to the other programs.