Abstract

In this research work, we propose a cooperative approach called simulated particle swarm optimization (SPSO) which is based on metaheuristics to find an approximate solution for the multiple sequence alignment (MSA) problem. The developed approach uses the particle swam optimization (PSO) algorithm to discover the search space globally and the simulated annealing (SA) technique to improve the population leader «gbest» quality in order to overcome local optimum problem. Simulation results on BaliBASE benchmarks have shown the potent of the proposed method to produce good quality alignments comparing to those given by other existing methods.

Highlights

  • Multiple sequence alignment (MSA) is a crucial tool in molecular biology and genome analysis

  • Simulation results using SP score measure and nine BaliBASE tests case showed that the proposed BPSO algorithm has superior performance when compared to ClustalW and SAGA algorithms

  • The proposed hybrid simulated particle swarm optimization (SPSO) algorithm consists of applying Particle Swarm Optimization (PSO) algorithm in order to guide global search, and use simulated annealing (SA) to improve the gbest which helps PSO to escape from local optimum and increase the convergence speed of SPSO algorithm

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Summary

Introduction

Multiple sequence alignment (MSA) is a crucial tool in molecular biology and genome analysis. It has been considered as one of the important tasks in bioinformatics [1]. Discovering optimal alignment in multiple biological sequence data is known as a NP-complete problem [3]. It has been identified as a combinatorial optimization problem [4], which is solved by using an exact or approximate algorithms. In [11], the authors proposed a novel approach to multiple sequence alignment based on Particle Swarm Optimization (PSO) to improve a sequence alignment previously obtained using Clustal X. The authors in [13] proposed an algorithm based on binary PSO algorithm to address the multiple sequence alignment problem. Simulation results using SP score measure and nine BaliBASE tests case showed that the proposed BPSO algorithm has superior performance when compared to ClustalW and SAGA algorithms

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