Cosentino, Salvatore2; Larsen, Mette Voldby1; Aarestrup, Frank Møller5; Lund, Ole7
1 Department of Systems Biology, Technical University of Denmark2 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark3 Immunological Bioinformatics, Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark4 Integrative Systems Biology, Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark5 National Food Institute, Technical University of Denmark6 Division of Epidemiology and Microbial Genomics, National Food Institute, Technical University of Denmark7 Department of Bio and Health Informatics, Technical University of Denmark
Although the majority of bacteria are harmless or even beneficial to their host, others are highly virulent and can cause serious diseases, and even death. Due to the constantly decreasing cost of high-throughput sequencing there are now many completely sequenced genomes available from both human pathogenic and innocuous strains. The data can be used to identify gene families that correlate with pathogenicity and to develop tools to predict the pathogenicity of newly sequenced strains, investigations that previously were mainly done by means of more expensive and time consuming experimental approaches. We describe PathogenFinder (http://cge.cbs.dtu.dk/services/PathogenFinder/), a web-server for the prediction of bacterial pathogenicity by analysing the input proteome, genome, or raw reads provided by the user. The method relies on groups of proteins, created without regard to their annotated function or known involvement in pathogenicity. The method has been built to work with all taxonomic groups of bacteria and using the entire training-set, achieved an accuracy of 88.6% on an independent test-set, by correctly classifying 398 out of 449 completely sequenced bacteria. The approach here proposed is not biased on sets of genes known to be associated with pathogenicity, thus the approach could aid the discovery of novel pathogenicity factors. Furthermore the pathogenicity prediction web-server could be used to isolate the potential pathogenic features of both known and unknown strains.