Our work developing algorithms for metagenome analysis includes the development of new analysis methods as well as contributing to community-driven efforts to evaluate the performance of publicly available metagenome data analysis methods.
We have developed advanced DNA sequencing technologies via tightly coupled development of computational inference methods and next-generation sequencing techniques.
We are also working to understand how the infant’s microbiome develops, in humans and in animals, and its relationship with maternal microbiota and infant health status.
The team is working to develop new, scalable phylogenetic analysis methods using Monte Carlo methods, including Variational Inference, Sequential Monte Carlo, MCMC, and approximate Bayesian computation. We have a particular interest in the application of these methods to bacteria, and the analysis challenges introduced by bacterial recombination and horizontal gene transfer.