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Numerical characterization of DNA sequences based on the k ‐step Markov chain transition probability
Author(s) -
Dai Qi,
Liu XiaoQing,
Wang TianMing
Publication year - 2006
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20471
Subject(s) - markov chain , transition (genetics) , mathematics , series (stratigraphy) , sequence (biology) , dna , chain (unit) , characterization (materials science) , combinatorics , invariant (physics) , additive markov chain , discrete mathematics , markov model , variable order markov model , genetics , physics , biology , statistics , gene , astronomy , mathematical physics , optics , paleontology
A DNA sequence can be regarded as a discrete‐time Markov chain. Based on k ‐step transition probabilities, we construct a series of 4 × 4 k ‐step transition matrices to characterize the DNA primary sequences. According to the properties of Markov chains, we obtain distributions of A, T, C and G, and analyze the changes among them from yesterday to tomorrow. We can calculate the probabilities of nucleotide triples of DNA primary sequences. Finally, we introduce a correlation of this kind of transition matrices and consider it as an invariant to analyze the similarities/dissimilarities of DNA sequences. © 2006 Wiley Periodicals, Inc. J Comput Chem, 2006