GAknot: RNA secondary structures prediction with pseudoknots using genetic algorithm

Abstract

Predicting RNA secondary structure is a significant challenge in Bioinformatics especially including pseudoknots. There are so many researches proposed that pseudoknots have their own biological functions inside human body, so it is important to predict this kind of RNA secondary structures. There are several methods to predict RNA secondary structure, and the most common one is using minimum free energy. However, finding the minimum free energy to predict secondary structure with pseudoknots has been proven to be an NP-complete problem, so there are many heuristic approaches trying to solve this kind of problems. In this paper, we propose GAknot, a computational method using genetic algorithm (GA), to predict RNA secondary structure with pseudoknots. GAknot first generates a set of maximal stems, and then it tries to generate several individuals by different combinations of stems. After halting condition is reached, GAknot will output the best solution as the output of predicted secondary structure. By using two commonly used validation data sets, GAknot is shown to be a better prediction method in terms of accuracy and speed comparing to several competitive prediction methods. Source code and datasets can be downloaded.