Nucleic Acids Research Advance Access originally published online on December 14, 2006
Nucleic Acids Research 2007 35(2):e12; doi:10.1093/nar/gkl1024
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Nucleic Acids Research, 2007, Vol. 35, No. 2 e12
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Methods Online |
A pHMM-ANN based discriminative approach to promoter identification in prokaryote genomic contexts
1 School of Engineering and Information Technology, Deakin University Victoria, Australia 2 Australian Research Council Centre in Bioinformatics Melbourne, Australia 3 Institute for Infocomm Research Singapore 119613
*To whom correspondence should be addressed. Tel: +61 3 92517684; Fax: + 61 3 92517604; Email: phoebe{at}deakin.edu.au
Received September 13, 2006. Revised October 25, 2006. Accepted November 14, 2006.
The computational approach for identifying promoters on increasingly large genomic sequences has led to many false positives. The biological significance of promoter identification lies in the ability to locate true promoters with and without prior sequence contextual knowledge. Prior approaches to promoter modelling have involved artificial neural networks (ANNs) or hidden Markov models (HMMs), each producing adequate results on small scale identification tasks, i.e. narrow upstream regions. In this work, we present an architecture to support prokaryote promoter identification on large scale genomic sequences, i.e. not limited to narrow upstream regions. The significant contribution involved the hybrid formed via aggregation of the profile HMM with the ANN, via Viterbi scoring optimizations. The benefit obtained using this architecture includes the modelling ability of the profile HMM with the ability of the ANN to associate elements composing the promoter. We present the high effectiveness of the hybrid approach in comparison to profile HMMs and ANNs when used separately. The contribution of Viterbi optimizations is also highlighted for supporting the hybrid architecture in which gains in sensitivity (+0.3), specificity (+0.65) and precision (+0.54) are achieved over existing approaches.