Affiliations: Department of Computer Engineering and Informatics,
University of Patras, GR-26500, Rio, Patras, Greece | Abramson Family Cancer Research Institute, 412 Curie
Blvd. Philadelphia, PA 19104-6160, University of Pennsylvania, School of
Medicine, Philadelphia, USA | Department of Environmental and Natural Resources
Management, University of Ioannina, Greece
Abstract: Ab initio coding potential prediction in a bacterial genome is an
important step in determining an organism's transcriptional regulatory
function. Extensive studies of genes structure have been carried out in a few
species such as. Escherichia coli, fewer resources exist in newly
sequenced genomes like Wolbachia. A model of gene prediction trained on
one species may not reflect the properties of other, distantly related
prokaryotic organisms. These issues were encountered in the course of
predicting genes in the genome of Wolbachia, very important gramnegative
bacteria that form intracellular inherited infections in many invertebrates. We
describe a coding potential predictor based on artificial neural networks and
we compare its performance by using different architectures, learning
algorithms and parameters. We rely on a dataset of positive samples constructed
from coding sequences and on a negative dataset consisted of all the intergenic
regions that were not located between the genes of an operon. Both datasets,
positive and negative, were output as fasta formatted files and were used for
neural network training. The fast, adaptive, batch learning algorithm Resilient
propagation, exhibits the best overall performance on a
64input-10hidden-1output nodes multi-layer perceptron neural network.
