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Bioinformatics Advance Access published online on September 25, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl482
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received July 12, 2006
Revised August 28, 2006
Accepted September 11, 2006

Article

PromoterExplorer: an effective promoter identification method based on the AdaBoost algorithm

Xudong Xie 1, Shuanhu Wu 2, Kin-Man Lam 3, and Hong Yan 4 *

1 Department of Electronic Engineering, City University of Hong Kong, Hong Kong; Department of Electronic Engineering, Tsinghua University, Beijing, China
2 Department of Electronic Engineering, City University of Hong Kong, Hong Kong; School of Computer Science and Technology, Yantai University, China
3 Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong
4 Department of Electronic Engineering, City University of Hong Kong, Hong Kong; School of Electronic and Information Engineering, University of Sydney, NSW 2006, Australia

* To whom correspondence should be addressed.
Hong Yan, E-mail: h.yan{at}cityu.edu.hk


  Abstract

Motivation: Promoter prediction is important for the analysis of gene regulations. Although a number of promoter prediction algorithms have been reported in literature, significant improvement in prediction accuracy remains a challenge. In this paper, an effective promoter identification algorithm, which is called PromoterExplorer, is proposed. In our approach, we analyze the different roles of various features, i.e. local distribution of pentamers, positional CpG island features and digitized DNA sequence, and then combine them to build a high-dimensional input vector. A cascade AdaBoost-based learning procedure is adopted to select the most "informative" or "discriminating" features to build a sequence of weak classifiers, which are combined to form a strong classifier so as to achieve a better performance. The cascade structure used for identification can also reduce the false positive.

Results: PromoterExplorer is tested based on large-scale DNA sequences from different databases, including the EPD, DBTSS, Genbank and human chromosome 22. Experimental results show that consistent and promising performance can be achieved.

Associate Editor: Alex Bateman
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