Fast clinical diagnostics of bacterial infections are essential, especially where an infection may be caused by a highly virulent bacterium such as Staphylococcus aureus. The genome of S. aureus is highly variable and can contain several mobile virulence determinants that affect the severity of infection and its treatment.
This project aims to evaluate and enhance the potential impact of automated comparative genomic analysis in clinical diagnostics in order to rapidly reveal clinically-relevant virulence determinants. The system uses raw Ion Torrent PGM sequencer reads as input and analyses them using an automatic eScience cloud-based workflow. The workflow makes predictions about a strain’s identity, biochemical features and capacity for virulence and antibiotic resistance.
We are currently working with Prof. Gould of the NHS to evaluate the potential of our system for impact in the diagnostics in a clinical setting. Several strains of S. aureus sourced from a variety of clinical situations and with known clinical phenotypes have been provided by the NHS in a blind trial. The identity of the strains and their phenotypes has been withheld in order to evaluate the potential of the system for impact in the diagnostics sector of the NHS and identify potential avenues for refinement of the system in this respect.