Multiple Sequence Alignment with Hidden Markov Model for Diabetic Genome

Abstract

Diabetes is one of the chronic diseases which occur when the pancreas is not able to secret insulin. Insulin is an important factor that transforms glucose in to energy. Analysing the multiple DNA sequence of diabetes is helpful in deriving more information about the disease. Profile Hidden Markov Model has a wide application in molecular biology. Thus we emphasized the use of PHMM for this Multiple Sequence Alignment (MSA). The main objective of this paper is to find the sequence pattern which the disease follows, estimating the parameters using Baum-Welch algorithm and finding the best optimal path using Viterbi algorithm. All valuable information from the sequences is obtained using PHMM.