I am a lecturer in the Department of Infectious Disease Epidemiology within the School of Public Health at Imperial College London. I am also a member of the MRC Centre for Outbreak Analysis and Modelling and of the NIHR Health Protection Research Unit in Modelling Methodology.
My research is focused on the analysis of genomic data in order to improve our understanding of bacterial evolution, epidemiology, ecology and pathogenicity. A key aim is to develop new bioinformatics and statistical methods that can handle the very large amounts of data made available by novel high-throughput sequencing techniques.
Because of the interdisciplinary nature of my work, I have broad interests in a variety of subjects, including theoretical topics such as mathematical population genetics, Bayesian statistics or Monte-Carlo methods, and biological topics such as bacterial evolutionary processes or pathogen epidemiology.
I have worked on a wide range of bacterial pathogens, especially those causing healthcare associated infections (eg Clostridium difficile and Staphylococcus aureus) and gastrointestinal infections (eg Salmonella enterica, Bacillus cereus, Campylobacter jejuni and Helicobacter pylori).
In 2007, I completed a doctorate in Statistical Genetics in the Department of Statistics, University of Oxford. I then spent three years as a postdoctoral research fellow in the Department of Statistics, University of Warwick. In 2010, I moved back to the University of Oxford as a leadership fellow in genomic microbiology before taking my current appointment with Imperial College London in April 2012. For more details see my personal webpage.
I have developed the following computer software, all of which are freely available on the internet:
- ClonalFrame: inference of bacterial microevolution using sequence data
- GenoPlast: analysis of genomic plasticity in bacteria
- SimMLST: Simulation of genetic data under a neutral model
- ClonalOrigin: inference of homologous recombination in bacteria using whole genome sequences
- TransPhylo: inference of a pathogen transmission tree given a phylogeny
- ClonalFrameML: efficient inference of recombination in whole bacterial genomes
I collaborate with the Modernising Medical Microbiology consortium which brings together the University of Oxford, the Health Protection Agency, the Sanger Institute and the NHS, with the aim of revolutionizing our understanding of human pathogens through the use of whole-genome sequencing technology. Three major bacterial pathogens are under particular investigation: Staphylococcus aureus, Clostridium difficile and Mycobacterium tuberculosis.
Other frequent collaborators include:
- Mark Achtman (University of Warwick)
- Aaron Darling (University of Technology Sydney)
- Daniel Falush (Swansea University)
- Dan Lawson (University of Bristol)
- Martin Maiden (University of Oxford)
- Tim Read (Emory University)
- Sam Sheppard (Swansea University)
- Sebastian Suerbaum (Hannover Medical School)
- Danny Wilson (University of Oxford)
Ansari MA, Didelot X, 2016, Bayesian Inference of the Evolution of a Phenotype Distribution on a Phylogenetic Tree., Genetics, Vol:204, Pages:89-98
et al., 2016, Genomic Analysis and Comparison of Two Gonorrhea Outbreaks., Mbio, Vol:7
Whittles LK, Didelot X, 2016, Epidemiological analysis of the Eyam plague outbreak of 1665-1666, Proceedings of the Royal Society B: Biological Sciences, Vol:283, ISSN:0962-8452
et al., 2016, Within-host evolution of bacterial pathogens, Nature Reviews Microbiology, Vol:14, ISSN:1740-1526, Pages:150-162
et al., 2015, Measurably evolving pathogens in the genomic era, Trends in Ecology & Evolution, Vol:30, ISSN:0169-5347, Pages:306-313
et al., 2015, The Role of China in the Global Spread of the Current Cholera Pandemic, Plos Genetics, Vol:11, ISSN:1553-7404
Didelot X, Wilson DJ, 2015, ClonalFrameML: Efficient Inference of Recombination in Whole Bacterial Genomes, PLOS Computational Biology, Vol:11, ISSN:1553-734X
Croucher NJ, Didelot X, 2015, The application of genomics to tracing bacterial pathogen transmission, Current Opinion in Microbiology, Vol:23, ISSN:1369-5274, Pages:62-67