Biometrics and Bioinformatics

Saikat Kar ¹, Shiladitya Saha ¹, Tamal Chakrabarti ¹

Affiliations
1 Computer Science and Engineering Department, Institute of Engineering and Management, Y-12, Block-EP, Sector-V, Salt Lake Electronics Complex, Kolkata-700091, West Bengal, IN

Abstract

Recent studies in Bioinformatics have experienced a fundamental swing away from the traditional research and development methodologies based on Biology’s empirical ischolar_mains to large-scale, computer-based and automated tools and techniques. Today it is systematic and predictive with genomics, informatics, automation, and miniaturization all playing a role. This blending of Biology and Information Science is likely to remain and magnify for a conceivable time. DNA sequence alignment is a frequently witnessed and discussed problem in Bio-informatics, where the researcher is aligning two or more DNA sequences to find common parts in them. Since the DNA sequences are extremely long (e.g. the human genome alone has approximately three billion DNA base pairs), sequence alignment becomes a challenging problem. This paper introduces an ant colony based solution to the DNA multiple sequence alignment problem.

Keywords

Bioinformatics, DNA, Sequence, Alignment, Ant Colony.

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Tamal Chakrabarti ¹, Sourav Saha ², Devadatta Sinha ³

Affiliations
1 Computer Science and Engineering Department, Institute of Engineering and Management, Y-12, Block-EP, Sector-V, Salt Lake Electronics Complex, Kolkata-700091, West Bengal, IN
2 Computer Science and Engineering Department, Institute of Engineering and Management, Y-12, Block-EP, Sector-V, Salt Lake Electronics Complex, Kolkata-700091, West Bengal, IN
3 Computer Science and Engineering Department, Calcutta University, 92 Acharya Prafulla Chandra Road, Kolkata-700009, West Bengal, IN

Abstract

Contemporary researchers of Bio-informatics have witnessed an exponential growth in the amount of biological information over the years. The increasing volume of DNA sequences has of late created interest among many scientists in computational approaches to DNA sequence analysis. A lot of computer analysis of DNA sequences is directed toward meaningful interpretation of biologically significant patterns. Pattern classification forms one of the most important foundations for extraction of knowledge from the enormous DNA sequence databases. This paper reports a cheap and efficient DNA pattern classifier based on the sparse network of Cellular Automata.

Keywords

Bio-Informatics, Cellular Automata, DNA, Pattern Classification, Sequence Analysis.

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