Cuiting Yan¹ , Qingsheng Huang² , Fen Zhang¹ , Ying Fang¹

1 School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, 510006, P. R. China
2 School of life sciences, Sun Yat-sen University, Guangzhou, 510275,P. R. China
Author    Correspondence author
International Journal of Molecular Evolution and Biodiversity, 2012, Vol. 2, No. 1   doi: 10.5376/ijmeb.2012.02.0001
Received: 07 May, 2012    Accepted: 22 May, 2012    Published: 15 Jun., 2012

This article was first published in Genomics and Applied Biology (2012, 31(3): 212-221) in Chinese, and here was authorized to translate and publish the paper in English under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Yan et al., 2012, A Novel Method for Evolution Analysis based on Image Registration, Vol.2, No.1 (doi: 10.5376/ijmeb.2012.02.0001)

Image registration is an important technique in image processing, which could be used to compare the similarity between two images. Here, a novel method based on transition probability matrix of oligonucleotide is proposed to infer the evolutionary relatedness of microbial organisms via image registration. Firstly, the oligonucleotide transition probability matrixes of microbial genomes are calculated by applying 1st order Markov Chain Method. Secondly, each transition probability matrix is converted into a color image, and then combined with each other to a joint histogram. Finally, the point set distribution of joint histogram is analyzed, and divergence formula is introduced and used as the similarity metric, which can reflect the evolutionary relatedness of organisms. For the organisms that taxonomic categories covered from kingdom to species, our results suggest that this new method is more accurate and discriminable than the methods based on single gene or oligonucleotide frequency especially for the classification under species. This method must be broadened and developed so that it can be applied to species identification and phylogeny inferring.

Image registration; Oligonucleotide transition probability matrix; Joint histogram divergence; Phylogenetic relationship