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Abstract: Viruses evolve very rapidly, on clinically relevant timescales. HIV evolves around repeated attacks from the host immune system to eventually cause AIDS. Seasonal influenza strains change from year to year, requiring updates to the influenza vaccine. New influenza strains can evolve from viruses that infect other species, causing global pandemic disease. Genome sequence data can provide an insight into all of these processes. Where evolution proceeds over a sufficient period of time, phylogenetic methods provide a framework for the analysis of data, but for shorter periods such approaches are inadequate. We here outline a maximum likelihood approach, using short-read data to evaluate the state, and over time the evolution, of a viral population. We illustrate the use of the method in understanding the potential evolution of pandemic influenza and its application to sequence data from a case of long-term influenza infection.

Speaker: Dr Chris Illingworth

Affiliation: Professor of Department of Genetics, University of Cambridge


Machine Learning Coffee Seminar is organized weekly by the Finnish Center for Artificial Intelligence FCAI. The location alternates between Aalto University and the University of Helsinki.