Discovering human and bacterial genes driving infection risk

external supervisor

Professor Martin Maiden, Department of Biology, University of Oxford

background

We aim to identify human and bacterial genes from large-scale genome sequencing data that influence important phenotypes including disease severity and drug resistance. We are generating new datasets, including joint genotyping of infected humans and the infecting bacteria, with the aim of identifying inter-species genetic interactions that influence disease outcome. We have developed innovative big data methods that improve on existing genome-wide association study (GWAS) approaches to maximize the chances of detecting signals. This studentship offers the opportunity to apply these powerful approaches to make new biological discoveries that change our understanding of the infection process.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

This project offers the student the opportunity to develop and apply big data approaches to human, bacterial and virus genomics to investigate one or more of (i) the influence of human-bacterial genetic interactions on the outcome of infections (ii) the potential of very large-scale open-source public databases to unlock bacterial genes underlying important traits including virulence and antimicrobial resistance (iii) the impact of gene expression variation on the progression of infectious disease within patients. There is opportunity to become familiar with a range of bacterial pathogens including Staphylococcus aureus, Neisseria meningitidis and Mycobacterium tuberculosis. Visit www.danielwilson.me.uk/publications.html for examples of recent projects.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

Group members will join weekly lab meetings and attend conferences. Training support is available through a range of courses including in R, python and scientific computing, many through the Medical Sciences Division and the Department for Continuing Education.

PROSPECTIVE STUDENT

We accept academically excellent students with a variety of backgrounds including scientists and clinicians who have graduated with Bachelors or Masters degrees in biology, medicine, statistics, computing and mathematics. The best performing students have an overriding interest and understanding in the biological problems at hand, and they come with, or develop, numerical competence and a natural affinity for computer-based work.