University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > A Bayesian approach to inferring the phylogenetic structure of microbial communities: the case of a seasonal Antarctic lake

A Bayesian approach to inferring the phylogenetic structure of microbial communities: the case of a seasonal Antarctic lake

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If you have a question about this talk, please contact Mustapha Amrani.

Mathematical, Statistical and Computational Aspects of the New Science of Metagenomics

Co-authors: Daniel Falush (Max Planck Evolutionary Anthropology), Xavier Didelot (Imperial College London)

Whole-genome metagenomic sampling allows researchers to investigate microbial communities as they shape and are shaped by their environments. A particularly interesting case occurs when a species (or closely-related species) evolves within a set of samples. As the samples are often mixtures of these recently-evolved lineages, a statistical difficulty arises in attempting to infer the clonal history of this differentiation while accounting for the mixture among samples. In this talk, I present the lineage model, a Bayesian attempt to simultaneously infer both the underlying phylogeny and the sample mixing using metagenomic data. I discuss the assumptions of the model, how it differs from other approaches, and simulation results. I conclude with an extended example on Chlorobium limicola, an important photosynthetic bacteria, found to be the dominant species in samples from a seasonal lake in Antarctica.

Related Links: http://arxiv.org/abs/1306.6313 – The lineage model paper, currently under review

This talk is part of the Isaac Newton Institute Seminar Series series.

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