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DNA Sequence Alignment Based on Intelligent Algorithm

Author: LiFangJie
Tutor: LiuXiYu
School: Shandong Normal University
Course: Management Science and Engineering
Keywords: Pairwise sequence alignment Artificial Fish School Algorithm Ant Colony Algorithm
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 85
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Abstract

The computer molecular biology is an interdisciplinary, computer, network tools, mathematics, information science, biology theory, methods, and techniques to the study of biological macromolecules. The bioinformatic purpose is to reveal the fundamental law of the genetic and functional information, as well as the complexity of the structure of the genomic information to further explain the genetic language. One of the basic information processing method bioinformatics sequence alignment between biological sequence evolution, function and structure information can be found to provide a theoretical basis for bioinformatics. Sequence alignment analysis was initially raised by the study of biological homology, after as technology development, the scope of its application and more widely. This article focuses on the basic idea of ??the background knowledge of molecular biology, DNA sequence alignment of the basic principles of artificial fish swarm algorithm and ant colony algorithm. First introduced sequence than the basic issues involved: basic operations, related definition, gap penalty and substitution matrix, and then introduce a pairwise sequence alignment algorithms: NW algorithm, Smith-Waterman algorithm and BLAST algorithm for multiple sequence alignment basic algorithm: progressive alignment algorithms and iterative algorithm. Finally, the artificial fish swarm algorithm used in the DNA double sequence alignment through experiments to prove the feasibility of the algorithm. The classic ant colony algorithm improvements and improved intelligent ant colony algorithm is applied in a pairwise sequence alignment, have a significant improvement in the speed and accuracy of the experiment show that the algorithm. Artificial fish swarm algorithm basic idea of ??the computing process and applications in chapter four of its application in DNA sequences than in proposed based on artificial fish swarm algorithm for DNA sequences than on coding model computing function crossover function, such as artificial fish variogram computation processes by simulating experiments prove the feasibility of the algorithm, the final than the comparison of results NW algorithm to compare the experimental results with the NW algorithm The results are the same. Chapter V, for the ant colony algorithm to search longer, slow convergence and easy to fall into local optimum value and shortcomings, the improved method. The combination of classical ant colony algorithm for DNA sequence alignment of the model, the improved ant colony algorithm is applied in double sequence alignment. Improved intelligent ant colony algorithm to dynamically update the character matching matrix, two-dimensional pheromone matrix into a three-dimensional pheromone matrix transition probability calculations are more accurate; dynamically updated when the ants select the path, a random number, so the ants search path, you can improve the convergence speed, and can effectively prevent falling into local optimal value; conducting pheromone update by the the ant path of score update pheromone formula, in order to ensure better and quicker to find the optimal value. Experimental results show that there has been a marked improvement in the algorithm and the basic ant colony algorithm, when compared to other improved algorithm, the convergence speed and accuracy.

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