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ISSN: 2155-6180

Journal of Biometrics & Biostatistics
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Research Article

Locating CpG Islands with Kullback-Leibler Divergence

Yung-Pin Chen*, Andrew Dittmore, Yasuhiro Goda, Alicia Laughton and Jessica Minnier

Department of Mathematical Sciences, Lewis & Clark College, Portland, USA

*Corresponding Author:
Yung-Pin Chen
Department of Mathematical Sciences
Lewis & Clark College, Portland, USA
E-mail: [email protected]

Received date: May 24, 2012; Accepted date: June 21, 2012; Published date: June 23, 2012

Citation:Chen YP, Dittmore A, Goda Y, Laughton A, Minnier J (2012) Locating CpG Islands with Kullback-Leibler Divergence. J Biomet Biostat 3:148 doi:10.4172/2155-6180.1000148

Copyright: © 2012 Chen YP, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

A CpG island is a short contiguous DNA subsequence that is rich in CG dinucleotides. CpG islands are often located around the promoters of housekeeping genes and have been found associated with certain tissue-specific genes. This observation indicates that they can be used as markers to identify genes. The information about the locations of CpG islands can also help us understand a gene regulation process called methylation. In this report, we propose a statistical method for locating CpG islands. Our method employs the Kullback-Leibler divergence. We use the given DNA sequence to determine a window size and a shift size for computing the divergence values along a DNA segment. A region in the proximity of a CpG island should contain consecutive windows with high divergence values. The distribution of the Kullback-Leibler divergence values can be suitably fitted by a truncated Pareto distribution. We estimate the parameters of the truncated Pareto distribution via the maximum likelihood principle. Then the fitted distribution is applied to locate regions with a divergence value exceeding a threshold level of significance. To assess the accuracy of our method, we compare our results to the putative CpG islands found in four well-studied mouse and human DNA sequences. The comparison suggests our approach consistently yields reliable predictions of CpG island locations.

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