Over time, organisms’ DNA sequences evolve in response to their changing environments, competitors and predators. By comparing similar gene sequences between species and individuals, we can use patterns of these changes to infer their evolutionary history – for instance, when did two species diverge? Which genes were most important for their survival? How many individuals were there? How have they spread across the globe?
These ‘phylogenetic’ studies have now shifted into a whole new gear as both computing power and sequencing ability (the speed and cost to read letters of DNA from a genome) have expanded by several orders of magnitude. We’re discovering that, although the basic principles of molecular evolution hold true, the variety and detail by which these patterns are realised in DNA sequences reflect the infinite multiplicity of physical forms seen in the natural world. In particular, it turns out that – against all expectations – sequences in unrelated organisms (for example bats and dolphins) can, occasionally, become more similar over time, not less. I exploit the power of massively parallel DNA sequencing and computing clusters to build a statistical picture of evolution across whole genomes: and we’re finding exceptions to the every rule in the book…