Fragment Assembly Based Fast and Optimal DNA Sequencing

Abstract

Growth of healthcare systems has resulted in growth of personalized medicine. Genome sequencing is one of the major players that can enable personalized medicine. The huge computational requirement of this process has made this facility costly and unaffordable for many. DNA sequencing methods that can be performed at computationally low cost and with better performance are sought. The first model presents particle swarm optimization (PSO) and cuckoo search (CS) based models and analyzes their performance levels on sequencing DNA. The sequence assembly is performed using particle swarm optimization (PSO) and cuckoo search (CS). The work then analyzes the pros and cons of using PSO and CS to determine the most effective model. The second method presents the approximate matching model for DNA sequence assembly. The third technique proposes a MapReduce based highest exact matches which successfully exploits and maps between DNA sequences using parallel index method.