title: Weighted relative entropy for alignment-free sequence comparison based on Markov model.
abstract: In this paper, we introduce a probabilistic measure for computing the similarity between two biological sequences without alignment. The computation of the similarity measure is based on the Kullback-Leibler divergence of two constructed Markov models. We firstly validate the method on clustering nine chromosomes from three species. Secondly, we give the result of similarity search based on our new method. We lastly apply the measure to the construction of phylogenetic tree of 48 HEV genome sequences. Our results indicate that the weighted relative entropy is an efficient and powerful alignment-free measure for the analysis of sequences in the genomic scale.
authors.affiliation: School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, PR China. gschang@mail.neu.edu.cn
authors.forename: Guisong
authors.initials: G
authors.lastname: Chang
authors.forename: Tianming
authors.initials: T
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D019277:Entropy; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D008969:Molecular Sequence Data; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA / Q000379:methods*