Titre du document / Document title

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

Auteur(s) / Author(s)

FEI NI TUANG ⁽¹⁾ ; RADEMAKER J. L. W. ⁽²⁾ ; ALOCILJA E. C. ⁽¹⁾ ; LOUWS F. J. ^{(2 3)} ; DE BRUIJN F. J. ^{(2 3 4)} ;

Affiliation(s) du ou des auteurs / Author(s) Affiliation(s)

⁽¹⁾ Department of Agricultural Engincering, Michigan State University, East Lansing, MI 48824, ETATS-UNIS
⁽²⁾ MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, ETATS-UNIS
⁽³⁾ NSF-Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, ETATS-UNIS
⁽⁴⁾ Department of Microbiology, Michigan State University, East Lansing, MI 48824, ETATS-UNIS

Résumé / 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.

Revue / Journal Title

FEMS microbiology letters   ISSN 0378-1097   CODEN FMLED7 

Source / Source

1999, vol. 177, n^o2, pp. 249-256 (24 ref.)

Langue / Language

Anglais

Editeur / Publisher

Blackwell, Oxford, ROYAUME-UNI  (1977) (Revue)

Mots-clés anglais / English Keywords

Bacteria ; Plant pathogen ; Identification ; Method ; Polymerase chain reaction ; Fingerprint method ; Neural network ; Backpropagation ; Computerized processing ;

Mots-clés français / French Keywords

Bactérie ; Phytopathogène ; Identification ; Méthode ; Réaction chaîne polymérase ; Méthode fingerprint ; Réseau neuronal ; Rétropropagation ; Traitement informatique ;

Mots-clés espagnols / Spanish Keywords

Bacteria ; Fitopatógeno ; Identificación ; Método ; Reacción cadena polimerasa ; Método fingerprint ; Red neuronal ; Retropropagacíon ; Tratamiento informático ;

Localisation / Location

INIST-CNRS, Cote INIST : 17567 A, 35400008937006.0100