Genomic Solutions to Hospital-Acquired Bacterial Infection Identification

Abstract

Hospital acquired infections (HAIs) are notorious for their likelihood of fatal outcomes in infected patients due to rapid bacterial mutation rates, consequent resistance to antibiotic treatments and stubbornness to treatment, let alone eradication, to the point they have become a challenge to medical science. A fast and accurate method to identify HAI will assist in the diagnosis and identification of appropriate patient treatment and in controlling future outbreaks. Based on recently developed new methods for genomic data extraction, representation and analysis in bioinformatics, we propose an entirely new method for species identification. The accuracy of the new methods is very competitive and in several cases outperforms the standard spectroscopic protein-based MALDI-TOF MS commonly used in clinical microbiology laboratories and public healthcare settings, at least prior to translation to a clinical setting. The proposed method relies on a model of hybridization that is robust to frameshifts and thus is likely to provide resilience to length variability in the sonication of the samples, probably one of the major challenges in a translation to clinical settings.