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Archaea and Bacteria

Laura Hug
University of Waterloo

Microbial diversity through a total community sequencing lens

Laura Hug
Laura Hug

Total community approaches (omics) provide a blueprint of the microbial functions and community diversity within an environment. With genome-resolved metagenomics, this view can be refined, identifying an organism's specific contributions to pathways and processes as well as their interactions with other community members. This approach has led to a recent explosion of genome sequences for uncultured and uncharacterized microbial lineages, many with previously-unknown roles in biogeochemical cycles. My work explores the environmental importance of these novel organisms and the emerging view of the Tree of Life that stems from our new understanding of microbial diversity.

Xavier Didelot
Imperial College London

Modelling recombination in prokaryote phylogenomics

Recombination happens frequently in most bacterial and archaeal species. Traditional phylogenetic techniques do not account for this, which can greatly limit their usefulness for the analysis of genomic data. The coalescent with gene conversion accurately models the ancestry process of prokaryotes, and this can be used to simulate realistic data, but it is too complex to use in an inferential setting. Approximations have therefore been introduced, which are centred around the concept of the clonal genealogy, that is the phylogeny obtained by following the line of ancestry of the recipient of each recombination event. I will review these mathematical models and ongoing efforts to develop statistical software to perform phylogenomic analysis in recombining prokaryotes.

Tim Vaughan
Auckland University

Joint Bayesian inference of bacterial ancestral recombination graphs

Homologous recombination is a central feature of bacterial evolution, yet confounds traditional phylogenetic methods. In this seminar I will present a novel approach to inferring bacterial evolution based on the ClonalOrigin model (Didelot et al., Genetics, 2010). This method permits joint Bayesian inference of the entire bacterial recombination graph and associated model parameters. The method is implemented in the BEAST 2 phylogenetic inference package. It can be easily combined with a variety of substitution models accounting for site-to-site clock rate heterogeneity as well as parametric and non-parametric models of effective population size dynamics. I will also present work on summarizing posterior distributions over the space of tree-based recombination graphs which, together with the joint inference method, aims to bridge the technological gap between recombination-aware phylogenetic inference and traditional methods.