Genetic Algorithm-Based Approach for RNA Secondary Structure Prediction

In recent years, bioinformatics has become an essential subject for molecular biological study. The various available algorithms are used for analyzing and integrating biological data. Among many biological statistics RNA (ribonucleic acid) is one of the most important as it is used in protein synthesis. In computational molecular biology, the optimal secondary structure prediction of large RNA is a problem being faced today. RNA sequences of some virus are very large in number which requires a large amount of time for secondary structure prediction. Consequently, parallelization of algorithm is one of the solutions to diminish time consumption. This paper proposes the algorithm GAfold for predicting secondary structure of RNA on shared memory multicore architecture. The various RNA sequences as an input have been taken from Gutell database. For calculating minimum free energy, thermodynamic model is used and the outcomes are compared with existing algorithms.