A Genetic Approach with Controlled Crossover and Guided Mutation for Biological Sequence Alignment

Abstract

Sequence alignment is one of the most useful strategies in bioinformatics. Biological sequences accumulate mutation through the process of evolution which eventually transforms the residues in the sequences. Primarily sequence alignment is performed to find the level of similarity of an unknown sequence with a known one by identifying common pattern of residues. The pair of sequences may be of equal or unequal length of DNA or protein sequences. In this article, we have proposed a novel Genetic Algorithm (GA) based alignment technique with modified crossover and mutation operations for finding the best alignment of a sequence pair in an optimized way. We have compared the performance of the proposed method analytically and statistically with some other well known and relevant sequence alignment techniques. The result shows the superiority of the proposed genetic method with modified operators over other sequence alignment approaches.