BIOLOGICAL GENE SEQUENCE STUCTURE ANALYSIS USING HIDDEN MARKOV MODEL

Identification or prediction of coding sequences from within genomic DNA has been a major part of the search of the gene. In this work real hidden Markov models (HMMs) to denote the consensus and deliver a beneficial tool in determining the splicing junction sites Markov models which has a recurring nature in computational biology leads to statistical models, in every sequential analysis it plays a role of putting up a right label on each residue. In sequential alignment and as well as in gene identification namely exons, introns or intergenic sequences which make in a sequence with homologous residue with the target database. Under the gene identification methodology Condon bias, exons, introns have length preference which leads to a combination of splice site consensus. Parameters are fixed on the onset while weight of the different information are polled together leading to the interception of result probability, which could lead to identifying the best score based on score mean and how confident are the best scoring answers are perfect. This leads to the concept of extendibility, to perfect and ad hoc gene finder, which is a modeled transitional methodology leading to the consensus, alternate splicing and offers polyadenylation signal. This leads to piling of authenticity against a delicate ad hoc program which could make to breakdown under its individual weightiness.