On the use of bootstrapped topologies in coalescent-based Bayesian MCMC inference: a comparison of estimation and computational efficiencies.

Coalescent-based Bayesian Markov chain Monte Carlo (MCMC) inference generates estimates of evolutionary parameters and their posterior probability distributions. As the number of sequences increases, the length of time taken to complete an MCMC analysis increases as well. Here, we investigate an approach to distribute the MCMC analysis across a cluster of computers.

The perils of plenty: what are we going to do with all these genes?

This new century's biology promises more of everything--more genes, more organisms, more species and, in short, more data. The flood of data challenges us to find better and quicker ways to summarize and analyse. Here, we present preliminary results and proofs of concept from three of our research projects that are motivated by our search for solutions to the perils of plenty.