A new greedy randomized adaptive search procedure for multiobjective RNA structural alignment

TL;DR

This paper introduces GRASPMORSA, a novel greedy randomized adaptive search procedure that optimizes multiple objectives for RNA structural alignment, improving accuracy in predicting conserved RNA structures.

Contribution

It presents a new multiobjective optimization method combining GRASP and local refinement for RNA structure alignment, demonstrating superior performance over existing approaches.

Findings

Effective in large-scale data sets

Achieves high-quality conserved RNA structure predictions

Outperforms some existing methods in accuracy

Abstract

RNA secondary structures prediction is one of the main issues in bioinformatics. It seeks to elucidate structural conserved regions within a set of RNA sequences. Unfortunately, finding an accurate conserved structure is a very hard task to do. Within the present study, the prediction problem is considered as a multiobjective optimization process in which the structural conservation and the sensitivity of the multiple alignment are optimized. The proposed method called GRASPMORSA is based on an aggregate function and GRASP procedure. The initial solutions are obtained by using a random progressive local/ global algorithm, and then they are refined by an iterative realignment. Experiments within a large scale of data have shown the efficacy and effectiveness of the proposed method and its capacity to reach good quality solutions.