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A new hybrid bio-inspired approach to resolve the multiple sequence alignment problem

Multiple sequence alignment (MSA) is one of the most important tools for analyzing information obtained from bioinformatics studies. It consists of detecting homologous characters between a set of protein or DNA sequences. This paper presents a new hybrid method named Quantum-biogeography based optimization for MSA problem (QBBO-MSA). In this method, a new mechanism is proposed, uses hill climbing algorithm to generate progressive initial alignments. Then, a Quantum representation is proposed to evolve the population with simulated annealing algorithm which controls the generation of new solutions and ensures good balance between exploration and exploitation mechanisms. In order to assess proposed approach performance, we have considered 26 datasets extracted from Balibase 2.0. The obtained results are compared with recent and well known methods namely, GAPAM, MSA-GA, CLUSTALW, SAGA and BBOMP. This comparison shows the effectiveness of the proposed approach.