DNA Sequence Alignment Based On Intelligent Algorithm

Computational Molecular Biology is an interdisciplinary which uses computers, Internet, Mathematics, Informatics and Biology as tools to deal with research on biomacromolecule. The purpose of Computational Molecular Biology is to reveal basic rules for genetic information, functional information and the complexity of genome structure. Sequence alignment is a basic information processing approach in Computational Molecular Biology. Sequence alignment can find genentic information, functional information and structural information. It can provide theoretical principle for Bioinformatics.This paper introduces background knowledge of Computational Molecular Biology, basic principles of DNA sequence alignment, Artificial Fish-swarm Algorithm as well as Ant Colony Algorithm. Firstly, it introduces the basic problem about sequence alignment like: basic operation, gap penalty and substitution matrix. Secondly, it introduces algorithm of DNA pair-wise sequence alignment, such as NW Algorithm, Smith-Waterman Algorithm as well as BLAST Algorithm. Thirdly, using Artificial Fish-swarm Algorithm to optimize DNA pair-wise sequence alignment is feasible by the simulated experiment. Meanwhile, in order to solve the disadvantages of Ant Colony Algorithm, this paper optimize standard Ant Colony Algorithm and apply it to DNA pair-wise sequence alignment. It proves that both convergence rate and accuracy have great better results.In this paper, The Artificial Fish-swarm Algorithm was de described in detail, such as fundamental principles, mechanisms as well as applications. The model of DNA pair-wise sequence alignment based on Artificial Fish-swarm Algorithm is proposed. This chapter put forward to the encoded method that can simplify the alignment. Fish-cross function and fish-variation function can have a great impact on behavior of artificial fish. Experiments show that the model of DNA pair-wise sequence alignment based on Artificial Fish-swarm Algorithm can generate a suitable result and get the optimum value the same as the result of NW Algorithm.As we all know, Ant Colony Algorithm has some disadvantages in convergence as well as local optimum value. Based on analysis of traditional Ant Colony Algorithm, this chapter put forward an improved Ant Colony Algorithm. In this algorithm, matching matrix dynamic update with the routes. Three dimension pheromone matrix instead of traditional pheromone matrix can attain a much more accurate result. Updating the viscosity of pheromone according to points can get the global optimum in less time. Experiments show that both convergence rate and global optimum have great better results by the model of DNA pair-wise sequence alignment based on the improved Ant Colony Algorithm.