Abstract: Genomic data on bacterial infections has revealed previously unsuspected levels of within-host diversity. Evolution during infection can help bacteria adapt to specific host conditions or anatomical niches, evade the host immune system and survive treatments with antibiotics or vaccines. Within-host evolution also has important implications for the reconstruction of transmission pathways. The diversity in genomic datasets reflects the phylogenetic relationships between isolates, but this phylogeny does not reflect transmission. Correctly accounting for within-host evolution is therefore crucial to perform genomic epidemiology of bacterial pathogens.

Biography: Xavier Didelot is a Senior Lecturer in the School of Public Health at Imperial College London. He previously received a doctorate in statistical genetics from the University of Oxford, worked as a research fellow at the University of Warwick and as a leadership fellow at the University of Oxford as part of the Modernising Medical Microbiology (MMM) consortium.

Xavier Didelot's research is concerned with developing statistically rigorous analysis methods for bacterial genomic data, in order to improve our understanding of bacterial evolution, ecology and epidemiology. He has experience with analysing genomic data from a broad range of bacterial pathogens, including hospital-associated infections (Clostridium difficile and Staphylococcus aureus), respiratory infections (Mycobacterium tuberculosis), enteric infections (Salmonella enterica and Helicobacter pylori) and sexually transmitted infections (Neisseria gonorrhoeae and Chlamydia trachomatis).

He is the author of several software packages, including ClonalFrame which is a phylogenetic method that accounts for the effect of recombination and TransPhylo which is a method that reconstructs transmission events from genomic data.