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Hepatitis C virus (HCV) causes chronic and potentially lifelong infection, and kills around 400,000 people worldwide every year. As a result of the development of new drugs in recent years, HCV is now treatable, but identifying newly infected individuals and targeting interventions remains a challenge. 'Cluster busting', by which new clusters of infections are identified by using molecular methods, has been successful in targeting new outbreaks of HIV infection. However, the within-host dynamics of HCV are much more complex than HIV infections, making cluster busting a more difficult challenge. During this studentship you will analyse HCV viral sequencing data from a clinical trial, including around 30 individuals who have longitudinal follow up. You will use different methods, including those accounting for within-host viral diversity, to identify clusters, and specifically to identify the most robust methods to identify clusters of sequences belonging to the same individual. Through this project you will learn how to work with whole-genome deep-sequencing data, and to use population and phylogenetic methods to cluster sequences.


10 weeks (July – Sept 2023). The student will be co-supervised by myself and my postdoc (Lele Zhao), and between us supervision will be available throughout the project.


Selection Criteria

This project would be suited for someone with a background in evolutionary biology or genomics, or a more mathematical/computational background, who can demonstrate an interest in this area of study.

Our team