Ed Feil

My research interests lie in using sequence data to study bacterial pathogens, both from a point of view of managing infections (molecular epidemiology) but also in terms of understanding fundamental evolutionary dynamics. The early part of my research career was spent generating and interpreting multilocus sequence typing (MLST) data for numerous species. I developed the BURST algorithm to help visualise these datasets (J Bacteriol. 186(5):1518-30). More recently, I have focussed on whole genome sequence (WGS) data, and was joint lead author on the first demonstration of WGS data for molecular epidemiology (Science 327: 5964 469-474).

I also have a strong interest in using whole genome sequencing (WGS) to understand and manage the spread of bacterial pathogens in animals, and in the evolution and transmission of antimicrobial resistance within a One-Health context.

In addition to molecular epidemiology, WGS data of bacterial pathogens can shed light on basic evolutionary questions concerning diversification; mutation, recombination and selection. Research questions include the strength of selection on intergenic sites, codon bias, context dependant mutation, the maintenance of GC content, INDEL formation, and gene essentiality.