Since the publication of the first complete genome sequence of a free-living organism (Haemophilus influenza Rd.) in 1995, the ease and costs of sequencing have plummeted to the extent that hundreds of thousands of genome sequences for bacteria, viruses and fungi are now publicly available. These data are providing unprecedented detail in how different evolutionary processes such as mutation, selection and horizontal gene transfer shape genetic diversity in natural populations. These data are also providing the means to detect the genetic bases of important phenotypes such as virulence, antibiotic resistance or adaptation to specific hosts or environments. However, the rate at which sequence datasets are growing easily outstrips the improvements in the costs and power of computers (as famously described by Moore’s Law), and this presents significant computational and analytical challenges. We are using and developing scalable bioinformatics tools and algorithms to meet these challenges, and to fully exploit the opportunities of the post-genomics era to address long-standing questions in both fundamental and applied microbial evolution.
We use and develop Bayesian and machine-learning approaches to detect genes and mutations conferring adaptation to specific environmental conditions, or for colonising specific hosts. We are also focussed on developing computer simulations describing gene flow between different strains or species, which is central to understanding ecological interactions as well as genomic processes. Network approaches are useful for understanding gene regulation, and how newly acquired genes become functionally integrated into the genome. Mathematical approaches also underpin our research on species interactions within mixed microbial communities, both in the environment (for example at a waste water treatment plant), or the microbiome of human or non-human hosts. We are developing methods for sampling that can improve our likelihoods of selecting regions of interest within bacterial genomes and are trialling novel longitudinal sampling for genome-wide association studies (GWAS).
In addition to addressing fundamental questions relating to microbial evolution, our research also addresses key questions relating to the surveillance and management of infectious disease. For example, we are involved in large pan-European projects aimed at tracking the spread of antibiotic resistant strains on local, national and continental scales. Our work also focusses on managing disease and vaccine development in commercially important animals, including farmed chicken and fish. Understanding the drivers for disease emergence and transmission in farming environments can inform on more sustainable animal husbandry and food production methods.We are also using sequencing and geospatial mapping to look at risk areas within the Rohingya refugee camps for waterborne pathogens.