Identification of bacterial rep-PCR genomic fingerprints using a backpropagation neural network

Abstract

A backpropagation neural network (BPN) was used to identify bacterial plant pathogens based on their genomic fingerprints. Genomic fingerprint data, comprised of complex DNA band patterns generated using BOX, enterobacterial repetitive intergenic consensus (ERIC) and repetitive extragenic palindromic (REP)-primers (rep-PCR), were used to train three independent BPNs. 10 Strains of the genus Xanthomonas, each with a characteristic host plant range, were identified correctly using the three trained BPNs. When tested with fingerprints of bacterial strains not present in the training sets, the rejection rate was 100%, using the three BPN classifiers combined. Thus, BPN protocols can be employed to generate a powerful computer-based system for the identification of pathogenic bacteria in the genus Xanthomonas.