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Intl. J. of Mol. Evol. and Biodivers. 2012, Vol. 2, No.1, 1-7
http://ijmeb.sophiapublisher.com
An Analysis Open Access
A Novel Method for Evolution Analysis based on Image Registration
Cuiting Yan
1
, Qingsheng Huang
2
, Fen Zhang
1
, Ying Fang
1
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
Corresponding author email:
yfang@scut.edu.cn;
Authors
International Journal of Molecular Evolution and Biodiversity, 2012, Vol.2, No.1 doi: 10.5376/ijmeb.2012.02.0001
Received: 14 April, 2011
Accepted: 2 May, 2011
Published: 08 June, 2011
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.
Preferred citation for this article:
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)
Abstract
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.
Keywords
Image registration; Oligonucleotide transition probability matrix; Joint histogram divergence; Phylogenetic relationship
Introduction
The traditional method of phylogenetic inference is
based on multiple sequence alignment of the
homologous genes for organisms (Wu and Eisen,
2008). Although this method has been applied widely
for phylogenetic studies, it has its limitations.
Inferring the phylogenetic relationship from any single
gene or several genes not only carries some risks, but
also would greatly depend greatly on whether the
selected genes can truly reflect the whole evolutionary
history of organisms. Furthermore, the traditional
method does not have high distinguish ability,
especially for taxa which is lower than the rank of
Species. For instance, 16S rRNA gene which is most
typically used for classification of bacteria, the
phylogenic tree based on 16S rRNA cannot estimate
the evolution distance between organisms of
intraspecies even though their clustering is correct
(Takahashi et al., 2009). Another way is compare the
differences of GC content or the oligonucleotide
relative abundance of complete genomes (Bohlin et al.,
2008a; 2008b). But this method is not comprehensive
because only the content or relative abundance of the
whole genome were taken into account, ignoring the
influence of adjacent oligonucleotide. Hence, Xiong et
al (2008) proposed an alternative method to
reconstruct phylogenetic trees based on information
from oligonucleotide frequency differences across
genomes. But their results indicated that the method
may be effective for the classification level which is
lower than Family(Xiong et al., 2008).
In this paper, we introduced a novel method based on
image registration to analyze evolutionary relatedness
of microbial organisms. In order to consider the
influence of adjacent oligonucleotide thoroughly,
Markov Chain Method was used for genomic analysis.
We hypothesized that the elongation of genomic