Typing Staphylococcus aureus Using the spa Gene and Novel Distance Measures

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 4; no. 4; pp. 693 - 704
• Main Authors: Agius, P., Kreiswirth, B.N., Naidich, S., Bennett, K.P.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2007; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bacteria; Bacterial Typing Techniques; Capacitive sensors; Cluster Analysis; clustering; Clustering algorithms; Clustering methods; Computational Biology - methods; Gene Expression Profiling; Gene Expression Regulation, Bacterial; Genomics; Genotype; genotyping; Hospitals; Immune system; Microorganisms; Models, Genetic; Models, Statistical; Models, Theoretical; Molecular Epidemiology - methods; moleuclar epidemiology; Oligonucleotide Array Sequence Analysis; Protein Interaction Mapping; Proteins; Reproducibility of Results; sequence algorithms; Sequences; Skin; Software; Staphylococcus aureus; Staphylococcus aureus - genetics; Studies; clustering; sequence algorithms; moleuclar epidemiology; genotyping; staphylococcus aureus
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/tcbb.2007.1053

We developed 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 bacterium using only its spa gene. Two of the sequence algorithms we used are well established, whereas 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 multilocus 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 discriminating than MLST, identifying new strains that the MLST method may not. Finally, we performed a multidimensional scaling of our distance matrices to visualize the relationship between isolates. The proposed methodology provides a promising new approach to molecular epidemiology.