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CLC number: TP391.1; R394.3

On-line Access: 2011-04-11

Received: 2010-04-11

Revision Accepted: 2010-07-05

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.4 P.263-272

http://doi.org/10.1631/jzus.C1000091


Structural visualization of sequential DNA data


Author(s):  Xiao-hong Mao, Jing-hua Fu, Wei Chen, Qian You, Shiao-fen Fang, Qun-sheng Peng

Affiliation(s):  The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310013, China, State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA

Corresponding email(s):   chenwei@cad.zju.edu.cn

Key Words:  Genome sequence, Sequential visualization, Bio-information visualization


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Xiao-hong Mao, Jing-hua Fu, Wei Chen, Qian You, Shiao-fen Fang, Qun-sheng Peng. Structural visualization of sequential DNA data[J]. Journal of Zhejiang University Science C, 2011, 12(4): 263-272.

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author="Xiao-hong Mao, Jing-hua Fu, Wei Chen, Qian You, Shiao-fen Fang, Qun-sheng Peng",
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A1 - Jing-hua Fu
A1 - Wei Chen
A1 - Qian You
A1 - Shiao-fen Fang
A1 - Qun-sheng Peng
J0 - Journal of Zhejiang University Science C
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1000091


Abstract: 
To date, comparing and visualizing genome sequences remain challenging due to the large genome size. Existing approaches take advantage of the stable property of oligonucleotides and exhibit the main characteristics of the whole genome, yet they commonly fail to show progression patterns of the genome adjustably. This paper presents a novel visual encoding technique, which not only supports the binning process (phylogenetic analysis), but also allows the sequential analysis of the genome. The key idea is to regard the combination of each k-nucleotide and its reverse complement as a visual word, and to represent a long genome sequence with a list of local statistical feature vectors derived from the local frequency of the visual words. Experimental results on a variety of examples demonstrate that the presented approach has the ability to quickly and intuitively visualize DNA sequences, and to help the user identify regions of differences among multiple datasets.

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