A pHMM-ANN based discriminative approach to promoter identification in prokaryote genomic contexts.
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.
Mann
Scott
S
School of Engineering and Information Technology, Deakin University, Victoria, Australia.
Li
Jinyan
J
Chen
Yi-Ping Phoebe
YP
D019295:Computational Biology / Q000379:methods; D004926:Escherichia coli / Q000235:genetics; D016680:Genome, Bacterial; D023281:Genomics / Q000379:methods*; D008390:Markov Chains; D016571:Neural Networks, Computer; D011401:Promoter Regions, Genetic