analysis of similarity of dna and protein sequences by alignment free method based on maximum entropy principle

Abstract

Bioinformatics comes in the category of interdisciplinary area, particularly mentioned for computational portion of molecular biology. When computers were not there to perform computations, researchers used living species or unreal environment to perform several experiments. With various biological experimentations huge amount of generatic data yield, this was not fit for use until we have to do proper analysis and classification. newlineThe Similarity analysis of DNA and Protein data results towards study of finding the biological databases, sequence categorization, structure as well as functional preservation and phylogenetic tree generation. The main motive of evolutionary study is to focus on states of system over a long time interval. But it is not practically possible to repeat these phylogenetic events in labs. So the methods for analyzing biological sequences are primarily depend on various statistical and computational approaches known as alignment free methods. newlineThe Composition Vector method comes in the category of alignment-free method for sequence comparison. Our proposed method shows a modified k-string method which utilizes the ratio of frequency of various common sub-words of length k to compare two sequences. In the present research, some improved formulas are proposed as well as we used entropy principle. Actual methods are wont to achieve a group of doable formulas from that we have a tendency to select the one that maximizes the entropy. These alternatives generate a form of unified technique towards comparison of DNA sequences. The results of proposed method also compared with previous composition vector and k-string method. newline