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​​Untangling mixed infections in malaria using genomes and MCMC​

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​​Malaria infections are often “complex”, meaning they consist of several parasite strains co-circulating in the blood. Complex infections can arise due to multiple mosquito bites, and/or due to a single bite introducing several genotypes. Complex infections create a huge problem when it comes to genetic sequencing because the “phasing” of the genotypic data is lost. This means a genetic variation at a first locus may be known, and there is variation at a second locus, but its unclear which variation goes together. The research to be discussed at this event aims to untangle the variation to get back to the original phased genomes, doing so allows researchers to look for patterns in drug resistance markers or relatedness between samples. This seminar will present a new software tool called “Tapestry” that was developed to solve this problem. As well as tacking an important obstacle in malaria genomics, this connects to deep problems in Markov chain Monte Carlo (MCMC) that apply across different model classes, including inference in highly multi-modal problems.​ 

Speaker

Robert Verity

​​Robert Verity, Imperial College London

​​Robert Verity is a Research Fellow in the malaria group at the Imperial College London MRC Centre for Global Infectious Disease Analysis. His research focuses on pathogen genomic epidemiology (PGE). Specifically, his objective is to use pathogen genomic data to improve our understanding of the spread of disease and to provide actionable information for decision makers. PGE is a fast-moving field, brought about by advances in high throughput sequencing technologies and rapidly dropping costs. This is creating an abundance of genomic data that has the potential to transform disease surveillance, but only if we can unlock this new signal. Doing so requires the development of new analysis methods, data structures, workflows, mathematical models, study designs and visualisation tools. Dr Verity’s work covers all these domains and involves working closely with data generation centres and control programmes.​ 

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