Abstract: Genomics is the determination of the complete DNA sequence of an organism. The objective of modern human genomics is preventive, predictive and individual medicine. In agriculture, the goal is the production of foods with improved production characteristics and increasingly beneficial consumer traits. Post-genomics refers to the biological processes that follow from DNA sequence. Efficient sequence alignment is one of the most important and challenging activities in bioinformatics. To perform and accelerate sequence alignment activities several algorithms have been proposed. Smith-Waterman algorithm represents a highly robust and efficient parallel computing system development for biological gene sequence. The research work here gives a deep understanding and knowledge transfer about existing approach for gene sequencing and alignment using Smith-Waterman algorithm their strength and weaknesses. Smith-Waterman algorithm calculates the local alignment of two given sequences used to identify similar RNA, DNA nucleotides. To identify the enhanced local alignments of biological gene pairs Smith-Waterman algorithm uses dynamic programming approach. It is proficient in finding the optimal local alignment considering the given scoring systems.
Keywords: Smith-Waterman, Dynamic Programming, Smith-Waterman Algorithm (SW), Needleman-Wunsch Algorithm (NW).