Global pairwise sequence alignment using Hidden Markov Models applied through different scoring schemes

Abstract

Hidden Markov Method in Bioinformatics is very popular since it proposed for the sequence analysis. This statistical method can be used from pairwise sequence alignment to database search. In this study, a global pairwise sequence alignment and database search using Hidden Markov Method are implemented. Although that can be solved by Dynamic Programming, the latter poses such a weakness that eventually leads to an excessive memory usage once all the possibilities are tried. Two different models are used to build Hidden Markov Model. The first one is untrimmed model and second is trimmed model. Additionally these models are compared through different scoring schemes.