Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 2, Pages: 1-7
H. Nabeel Kaghed1, S. Eman Al –Shamery2 and Fanar Emad Khazaal Al-Khuzaie2*
1Ministry of Higher Education and Scientific Research, Head of Supervision and Scientific Evaluation Apparatus, Hillah, Iraq; [email protected] 2Department of Software, College of Information Technology, University of Babylon, Hillah, Iraq; [email protected], [email protected],
*Author For Correspondence
Fanar Emad Khazaal Al-Khuzaie
Department of Software, College of Information Technology, University of Babylon, Hillah, Iraq; [email protected]
Background: Multiple Sequences Alignment (MSA) is the one of the most important research themes in bioinformatics as well known that the Genetic Algorithm (GA) working on finding the optimal solution, but it may take long generations to get to the solution and this is a problem especially in protein. Methods: In this research the dataset has been used in the form of Deoxyribonucleic acid (DNA) sequences, and protein sequences for the purpose of global alignment all these sequences belong to a human. Aligning several sequences cannot be done in polynomial time and therefore, heuristic methods such as GA can be used to found approximate solutions of MSA problems. Several algorithms based on genetic algorithms have been developed for this problem in recent years. In this paper, a new evaluation process, good mutation probability, new recombination operators, and a strong condition for termination have been used by developed genetic algorithm. Findings: The strength of the proposed GA is excellent matching and a good time with less storage. The experimental results show that the proposed algorithm is capable of finding good Multiple Sequence Alignment in contrast to a traditional genetic algorithm while it uses low computational complexity. The weaknesses of the traditional GA have been solved by the developed GA. Applications: Multiple sequence alignment by using genetic algorithm plays a vital role in our day-to-day life for various biological purpose especially in a clinical laboratory.
Keywords: Affine Gap Penalty, Genetic Algorithm, Global Sequence Alignment, Local Sequence Alignment, Multiple Sequence Alignment
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