Background: Worldwide effort on sampling and characterization of molecular variation within a large number of
human and animal pathogens has lead to the emergence of multi-locus sequence typing (MLST) databases as an
important tool for studying the epidemiology and evolution of pathogens. Many of these databases are currently
harboring several thousands of multi-locus DNA sequence types (STs) enriched with metadata over traits such as
serotype, antibiotic resistance, host organism etc of the isolates. Curators of the databases have thus the possibility
of dividing the pathogen populations into subsets representing different evolutionary lineages, geographically
associated groups, or other subpopulations, which are defined in terms of molecular similarities and dissimilarities
residing within a database. When combined with the existing metadata, such subsets may provide invaluable
information for assessing the position of a new set of isolates in relation to the whole pathogen population.
Results: To enable users of MLST schemes to query the databases with sets of new bacterial isolates and to
automatically analyze their relation to existing curated sequences, we introduce here a Bayesian model-based method
for semi-supervised classification of MLST data. Our method can use an MLST database as a training set and assign
simultaneously any set of query sequences into the earlier discovered lineages/populations, while also allowing some or
all of these sequences to form previously undiscovered genetically distinct groups. This tool provides probabilistic
quantification of the classification uncertainty and is highly efficient computationally, thus enabling rapid analyses of
large databases and sets of query sequences. The latter feature is a necessary prerequisite for an automated access
through the MLST web interface. We demonstrate the versatility of our approach by anayzing both real and synthesized
data from MLST databases. The introduced method for semi-supervised classification of sets of query STs is freely
available for Windows, Mac OS X and Linux operative systems in BAPS 5.4 software which is downloadable at http://web.
abo.fi/fak/mnf/mate/jc/software/baps.html. The query functionality is also directly available for the Staphylococcus aureus
database at http://www.mlst.net and shortly will be available for other species databases hosted at this web portal.
Conclusions: We have introduced a model-based tool for automated semi-supervised classification of new pathogen
samples that can be integrated into the web interface of the MLST databases. In particular, when combined with the
existing metadata, the semi-supervised labeling may provide invaluable information for assessing the position of a new
set of query strains in relation to the particular pathogen population represented by the curated database.
Such information will be useful both for clinical and basic research purposes.