Title: A computational perception of locating multiple longest common subsequence in DNA sequences

Authors: G. Tamilpavai; R. Sripathy Padhma; C. Vishnuppriya

Addresses: Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli-627007, Tamil Nadu, India ' Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli-627007, Tamil Nadu, India ' Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli-627007, Tamil Nadu, India

Abstract: Bioinformatics is an active research area which combines biological matter as well as computer science research. The longest common subsequence (LCS) is one of the indispensable issue to be unravelled viably in computational science. Discovering LCS is fundamental undertaking in deoxyribonucleic acid (DNA) arrangement investigation and other molecular biology. In this paper, new calculation for discovering LCS of two DNA successions and its area is proposed. The objective of this created framework is to discover the area and length of all subsequences which introduces in the two arrangements. To achieve this, DNA sequences are stored in an array and the comparison of DNA sequences are performed using matching algorithm. At the end of matching process, group of subsequence are obtained. Then the length and location of the matched subsequence are computed. After completing the matching process, longest common subsequence(s) is located. In this proposed work, maximally obtained length of LCS is 8. Finally, the computation time is calculated for locating LCS in DNA sequences. In addition to this, computation time is analysed by gradually increasing the length (in characters count) of DNA sequences from 100, 200, 300, 400 and 500. It concludes that computation time for locating LCS in various lengths of DNA sequences took few seconds difference only.

Keywords: computational biology; deoxyribonucleic acid; DNA; longest common subsequence; LCS; matching algorithm; bioinformatics; molecular biology; NCBI; Matlab; intelligent computing.

DOI: 10.1504/IJIE.2020.104648

International Journal of Intelligent Enterprise, 2020 Vol.7 No.1/2/3, pp.93 - 106

Received: 26 Jul 2018
Accepted: 11 Oct 2018

Published online: 24 Jan 2020 *

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