Before I moved to the private sector as a software developer, I was an academic researcher. This is a summary of some of the projects I worked on.

Inference and Visualisation of Haplotype Networks

Network structures are used in population genetics to summarise the genetic diversity in a population. They are usually displayed with edges (branches) representing a single nucleotide substitution between haplotype sequences, and nodes, representing haplotype sequences, scaled to indicate the observed number of samples of a particular sequence. They often contain phylogeographic information (where samples were collected), or associated phenotypic information. In short, these are an informative, compact way of summarising a lot of data. We're working on a new, intuitive method for inferring haplotype networks, which will be implemented along with some popular existing methods, in a standalone piece of software.

A Bayesian Framework for Simulation Experiments

Simulation is used in computational biology for several purposes, from validating computational methods to assessing how well statistical models fit real biological data. The choice of parameter values for a simulation will have a major impact on the outcome of a simulation, and different approaches to parameter selection have been used. Most commonly, authors either choose a few parameter combinations or exhaustively test all combinations of parameters in a “grid-search” framework. We have developed a Markov chain Monte Carlo sampling method to explore the distribution of the parameter space, given a particular experimental outcome. This method is easily implemented, and applicable to a wide range of problems that can be addressed by simulation.

The Future: Population Genetics of Microbes