Detecting aberrant strains in bacterial groups as an aid to constructing databases for computer identification

Software tools and databases for bacterial systematics and their dissemination via global networks

The dynamic expansion of the taxonomic knowledge base is fundamental to further developments in biotechnology and sustainable conservation strategies. The vast array of software tools for numerical taxonomy and probabilistic identification, in conjunction with automated systems for data generation are allowing the construction of large computerised strain databases. New techniques available for the generation of chemical and molecular data, associated with new software tools for data analysis, are leading to a quantum leap in bacterial systematics. The easy exchange of data through an interactive and highly distributed global computer network, such as the Internet, is facilitating the dissemination of taxonomic data. Relevant information for comparative sequence analysis, ribotyping, protein and DNA electrophoretic pattern analysis is available on-line through computerised networks. Several software packages are available for the analysis of molecular data. Nomenclatural and taxonomic 'Authority Files' are available from different sources together with strain specific information. The increasing availability of public domain software, is leading to the establishment and integration of public domain databases all over the world, and promoting co-operative research projects on a scale never seen before.

Numerical classification and identification of Acinetobacter genomic species

A total of 211 Acinetobacter strains (representing all currently recognized genomic species) were tested for 329 biochemical characters. Overall similarities of all strains were determined for 145 characters by numerical taxonomic techniques, the UPGMA algorithm and the S(SM)) and the S(J) coefficients as measures of similarity. Seven clusters (two or more strains) and three unclustered strains were recovered at a similarity level of 80.0% (S(SM). At this level a complete correspondence between phenotypic cluster and genomic species was found only for genomic species 12 (Ac. radioresistens). At higher similarity levels (84.0% to 84.6% (S(SM)), however, several subclusters were found, each representing a single genomic species. An exception were the strains belonging to the genetically closely related species of the Acinetobacter calcoaceticus-baumannii complex. These were recovered scattered in several subclusters. The degree of genomic relatedness between some DNA groups correlated with phenotypic similarities, especially for DNA group 8 (Ac. Iwoffii) and 15 of Tjernberg and Ursing, and for DNA group 4 (Ac. haemolyticus) and 6. For the majority of genomic species, two identification matrices were constructed consisting of 22 and 10 diagnostic characters, respectively. The correct identification rates for the matrices were 98.0% (22 tests) and 90.8% (10 tests) taking a Willcox probability > 0.9. For unambiguous identification of some genomic species, however, additional methods (preferably DNA-DNA hybridization or ribotyping) should be used.

Numerical classification and identification of Aeromonas genospecies

A total of 176 Aeromonas strains representing all currently characterized genospecies were tested for 329 biochemical characters. Overall similarities of all strains were determined by numerical taxonomic techniques, the UPGMA algorithm and the SSM and the SJ coefficients as measures of similarity. Sixteen clusters (two or more strains) and seven unclustered strains were recovered at the 93.5% similarity level (SSM). Genospecies 1, 4, 5, 6, 7, 9, 12 and 13 were largely represented by single phena, whereas strains of genospecies 2 and 3 were found in closely-related phena. Strains belonging to genospecies 8 formed two distinct biotypes. Strains belonging to genospecies 11 formed a subcluster within a cluster representing different genospecies. In general, similar groupings were obtained with the Jaccard coefficient at a similarity level of 80.0% (SJ) with minor changes in the definition of clusters. The phenetic data showed good correlation with the taxa defined by DNA/DNA hybridization and those obtained by multilocus enzyme analysis. For all genospecies (independent from cluster assignment) 30 diagnostic characters were selected to construct a matrix for probabilistic identification. The correct identification rate of the matrix was 71.51% taking a Willcox probability greater than 0.99, and 83.7% taking a Willcox probability greater than 0.9 as identification threshold levels.