Evolutionary Genomics in real time

We use genomic and metagenomic sequencing-based surveys, and population genetic tests for natural selection, to understand how different natural microbial populations adapt to their environments in real time. The overarching aim is to move from description (which taxa or genes are present in the environment?) to mechanism: what are the likely ecological functions of these taxa or genes, and how do they respond to natural selection over time? 

By establishing a long-term program of sampling and sequencing microbial populations, we will also learn to what extent microbial evolution is repeatable in the wild, eventually moving toward predictive models. Research is focused on two major microbial habitats: 

(1) freshwaters subject to seasonal cyanobacterial blooms, and 
(2) the human gut, focusing on individuals in Bangladesh infected with Vibrio cholerae. 

These habitats, while important in their own right because of their bearing on human health and environmental safety, are also particularly suited to:

- Quantifying the relative importance of physiological, compositional, and evolutionary change over the course of months and years in natural microbial populations. (e.g. on what time scales do individual genes or entire microbial genomes change in frequency due to selection or neutral processes, and on what time scales do migration and changes in gene expression predominate?)

- Developing 'population genomic' methods to identify genes, cellular pathways and microbial populations subject to evolutionary change over time due to natural selection.

- Inferring the ecological functions of selected genes and populations, based on correlation with environmental and biotic factors, with the eventual goal of using the genetic targets of selection as biomarkers (e.g. molecular diagnostics) and in predictive ecosystem models.

Computational method development.

Although much progress has been made, going from genomic, metagenomic or metatranscriptomic data to evolutionary or ecological insight is not a solved problem. In addition to using established methods to identify genomic signatures of positive selection, we are constantly evaluating and developing new methods.