Computational Genomics and Epidemiology

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Dr. Alice McHardy

The research of the group focuses on the data-driven analysis of biological questions, as well as method development to solve prediction problems for large biological data sets. To address problems of either medical or biotechnological relevance we are using statistical learning techniques and phylodynamic methods. The latter combine phylogenetic with epidemiological information to infer, for instance, the spatio-temporal dynamics of rapidly evolving populations. We apply these techniques to analyze genomic data of microbial communities (also known as metagenomic data), of influenza viruses and of cancer cells.

Our projects

In metagenomics, we are working on composition-based techniques for the taxonomic assignment of metagenome sequence fragments and novel methods for inference of functional and phenotypic relationships between protein families. A second focus of research is influenza evolution. Using statistical learning techniques and phylodynamic approaches, in one project we are investigating the short-term evolutionary dynamics of the virus. Here we are searching for determinants of viral fitness with relevance to selecting efficient vaccine strains for the seasonal influenza vaccine. Furthermore, we are working on detecting markers of diagnostic value from cancer genomics data.