Eukaryotic Gene Prediction by Spectral Analysis and Pattern Recognition Techniques

The problem of computational gene prediction in eukaryotic DNA is investigated. The discrete Fourier transform is used to reveal the periodicity of three which is present in the essential subregions of a gene. We introduce a novel method that allows to predict the position of genes in an optimal way (in the sense of minimal error probability) based on the complex Fourier values at the frequency 1/3. Our method is based on training and testing a bayesian classifier. We simulate gene sequences for training, apply the Fourier transform to the sequences, extract feature vectors from the spectral representation of the binary sequences and train classifiers to discriminate coding from non coding regions in the sequence. The classifier is tested on a real gene sequence where the coding and non coding regions are known