Typing Staphylococcus aureus using the spa gene and novel distance measures Submitted to Special Issue on Computational Intelligence Approaches in Computational Biology and Bioinformatics

We develop an approach for identifying groups or families of Staphylococcus aureus bacteria based on genotype data. With the emergence of drug resistant strains, S. aureus represents a significant human health threat. Identifying the family types efficiently and quickly is crucial in community settings. Here, we develop a hybrid sequence algorithm approach to type this bacteria using only its spa gene. Two of the sequence algorithms we used are well established, while the third, the Best Common Gap-Weighted Sequence (BCGS), is novel. We combined the sequence algorithms with a weighted match/mismatch algorithm for the spa sequence ends. Normalized similarity scores and distances between the sequences were derived and used within unsupervised clustering methods. The resulting spa groupings correlated strongly with the groups defined by the well-established Multi locus sequence typing (MLST) method. Spa typing is preferable to MLST typing which types seven genes instead of just one. Furthermore, our spa clustering methods can be fine-tuned to be more discriminative, to identify new strains that the MLST method may not. Finally, we performed a multidimensional scaling on our distance matrices to visualize the relationship between isolates. The proposed methodology provides a promising new approach to molecular epidemiology.