Title

CpGIF: an algorithm for the identification of CpG islands

Authors

Sujuan Ye¹, Asai Asaithambi¹ and Yunkai Liu^1,*

Affiliation

¹Department of Computer Science, University of South Dakota, Vermillion, SD, USA

Email

Yunkai.Liu@usd.edu; * Corresponding author

Article Type

Prediction Model

Date

received May 01, 2008; revised May 13, 2008; accepted May 15, 2008; published May 20, 2008

Abstract

CpG islands (CGIs) play a fundamental role in genome analysis and annotation, and contribute to improving the accuracy of promoter prediction. Besides, CGIs in promoter regions are abnormally methylated in cancer cells and thus can be used as tumor markers. However, current methods for identifying CGIs suffer from various drawbacks. We present a new algorithm for detecting CGIs, called CpG Island Finder (CpGIF), which combines the best features in the most commonly used algorithms and avoids their disadvantages as much as possible. Five public tools for CpG island searching are used to compare with CpGIF for the assessment of accuracy and computational efficiency. The results reveal that CpGIF has higher performance coefficient and correlation coefficient than these previous methods, which indicates that CpGIF is able to provide high sensitivity and specificity at the same time. CpGIF is also faster than those methods with comparable prediction accuracy.

Keywords

CpG islands; CpG dinucleotides; clustering algorithm

Citation

Ye et al., Bioinformation 2(8): 335-338 (2008)

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.