Statistical modelling of evolutionary dynamic processes
A key strand of my group's research is the development of new models and algorithms for the study of evolutionary dynamic processes in crop plants, with a particular focus on comparative genomics. For example, we are currently using stochastic processes to model marker insertion and introgression in plant germplasm. We can then use our model to estimate the underlying genetic structure of a germplasm collection characterised by molecular markers. In addition to our previous work on gene order algorithms, we are developing new models for gene content evolution with the aim of developing unified models of gene order, content and sequence evolution. This will enable us to test hypotheses regarding the evolution of key crop and model species. Although the goal of these projects is to gain a better understanding of the evolution of crop plants so that we can use them more effectively in cross-species inference, in the development of our models and algorithms my group also works with many collaborators on the analysis of organisms such as yeasts, viruses and bacteria.
Alongside our model and algorithm development, we aim to develop and publish accompanying software tools. We are committed to the development of open source software and so we endeavour to use only freely available languages and environments. Examples of our software packages are CGHdist (a C program for the estimation of genomic distance based on unbalanced gene content), RAT (a Java program for the high-throughput analysis of sequence recombination) and MPP (a Java/R program for the estimation of gene and marker content from CGH and TAM microarrays).
Underpinning our model and software development, we are involved in several infrastructure projects, developing new databases and data integration methodologies. Key projects include GERMINATE, ComparaGRID and MONOGRAM. Within GERMINATE, we are developing a generic plant data management system and associated analysis tools for genetic resources. The ComparaGRID project is driving the development of tools to integrate federated comparative genomic data using state-of-the-art computing technologies within a semantic web environment. The MONOGRAM Network is bringing together expertise and resources in cereal and grass research from across the UK in order to develop strategies and research programmes for crop improvement.
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LAST UPDATED: 8th May 2009