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Application of sliding-window discretization and minimization of stochastic complexity for the analysis of fAFLP genotyping fingerprint patterns of Vibrionaceae

Peter Dawyndt¹, Fabiano L. Thompson¹, Brian Austin², Jean Swings¹, Timo Koski³ and Mats Gyllenberg^4,

¹ Laboratorium voor Microbiologie, Universiteit Gent, B-9000 Gent, Belgium
² School of Life Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
³ Department of Mathematics, University of Linköping, S-58183 Linköping, Sweden
⁴ Department of Mathematics, University of Turku, FIN-20014 Turku, Finland

Correspondence
Peter Dawyndt
Peter.Dawyndt{at}ugent.be

Minimization of stochastic complexity (SC) was used as a method for classification of genotypic fingerprints. The method was applied to fluorescent amplified fragment length polymorphism (fAFLP) fingerprint patterns of 507 Vibrionaceae representatives. As the current BinClass implementation of the optimization algorithm for classification only works on binary vectors, the original fingerprints were discretized in a preliminary step using the sliding-window band-matching method, in order to maximally preserve the information content of the original band patterns. The novel classification generated using the BinClass software package was subjected to an in-depth comparison with a hierarchical classification of the same dataset, in order to acknowledge the applicability of the new classification method as a more objective algorithm for the classification of genotyping fingerprint patterns. Recent DNA–DNA hybridization and 16S rRNA gene sequence experiments proved that the classification based on SC-minimization forms separate clusters that contain the fAFLP patterns for all representatives of the species Enterovibrio norvegicus, Vibrio fortis, Vibrio diazotrophicus or Vibrio campbellii, while previous hierarchical cluster analysis had suggested more heterogeneity within the fAFLP patterns by splitting the representatives of the above-mentioned species into multiple distant clusters. As a result, the new classification methodology has highlighted some previously unseen relationships within the biodiversity of the family Vibrionaceae.

Published online ahead of print on 19 July 2004 as DOI 10.1099/ijs.0.63136-0.

An example of a stochastic-complexity-minimizing dendrogram for the fAFLP dataset of Vibrionaceae and the source code and a user manual for the BinClass software package are available as supplementary material in IJSEM Online.

Present address: Department of Mathematics and Statistics, Rdf Nevanlinna Institute, University of Helsinki, FIN-00014, Finland.