Pairwise sequence alignment of biological database using soft computing approach

Abstract

A Biological sequence alignment is the cornerstone of bioinformatics. The sequence alignment is carried out to extrapolate the evolutionary relationship among the living species which can help to characterize the functionality of unidentified sequences. The overall objective of pairwise alignment is to determine the uttermost alikeness among residues. Dynamic programming (DP) is the most popular technique for pairwise alignment but the downside of this approach is the proliferation of space and time complexity while handling considerable biological sequence. Various soft computing algorithms such as GA, PSO, ACO, GSA and many more, are in trend from past few years. These algorithms are inspired by natural evolution which helps to find near optimal solutions for optimization problem in reasonable amount of time. In this paper pairwise sequence alignment of protein is done using hybrid approach of soft computing algorithms which subsume Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO). The enactment of this hybrid approach is examined by comparing the simulation results with the DP based algorithms.