We’ve been working on viruses for a long time. We began with HIV, where we were interested in the evolution and dynamics within hosts. We then studied FIV, looking at the epidemiology within populations of stray and feral domestic cats. Most recently, we’ve been looking at Hepatitis B Virus, this time relating the within-host evolution of the virus to seroconversion.
Right now, we are working with Bill Abbott on a model of HBV e-antigen seroconversion.
Hayward, J. H. and Rodrigo, A. G. (2010). Molecular epidemiology of feline immunodeficiency virus in the domestic cat (Felis catus). Veterinary Immunology and Immunopathology 134:68-74
Lim SG, Cheng Y, Guindon S, Seet BL, Lee LY, Hu P, Wasser S, Tan T, Goode M, Rodrigo A. (2007) Viral quasispecies evolution in chronic hepatitis B: new light on an old story. Gastroenterology 33:951-8
Shankarappa, R., Margolick, R. B., Gange, S. J., Rodrigo, A. G., Upchurch, D., Farzadegan, H., Gupta, P., Rinaldo, C. R., Learn, G. H., He, X., Huang, X.-L., and Mullins, J. I. (1999) Consistent viral evolutionary changes associated with progression of HIV-1 infection. Journal of Virology 73:10489-10502.
Because of our work on viruses, we are interested in how standard population and evolutionary genetics apply to rapidly evolving populations. In collaboration with Joe Felsenstein, the serial-coalescent was introduced. Subsequently, we developed an MCMC-based Bayesian inference framework for analyzing sequences from serially sampled populations.
We are now working on extending these methods to next-generation sequences.
Goode, M., Guindon, S. and Rodrigo, A. G. (2008). Modelling the evolution of protein coding sequences sampled from Measurably Evolving Populations. Genome Informatics 21:150-164
Drummond, A., Nicholls, G.K., Rodrigo A.G. and Solomon.W. (2002) Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data. Genetics 161:1307-1320
Rodrigo, A. G. and Felsenstein, J. (1999). Coalescent Approaches to HIV-1 Population Genetics. In The Evolution of HIV (ed. Crandall, K.A.). Johns Hopkins University Press.
A ton of work on phylogenetics stretching back to the early 1990s. There are a variety of methods that we have worked on. Presently, we are interested in phylogeography, and appropriate ways to test phylogeographic hypotheses.
Steel, M. A. and Rodrigo, A. G. (2008). Maximum likelihood supertrees. Systematic Biology. 57:243-250
Guindon, S., Rodrigo, A. G., Dyer, K., Huelsenbeck, J. P. (2004) Modeling the site-specific
variation of selection patterns along lineages. Proceedings of the National Academy of Sciences, USA.101:12957-12962
Goldman, N., Anderson, J.P., and Rodrigo, A.G. (2000) Likelihood-Based Tests of Topologies in Phylogenetics. Systematic Biology 49:652-670.
Stepping away from evolutionary analyses, we have also worked on novel methods of statistical analyses in non-evolutionary bioinformatics. We are not doing anything on that front currently, but if the right problem presents itself, we may be tempted!
Wu, S. H., Black, M. A., North, R. A., Rodrigo, A. G. (2012). A Bayesian model for classifying all differentially expressed proteins simultaneously in 2D PAGE gels. BMC Bioinformatics 13:137
Wu, S.H., Black, M.A., North, R. A., Atkinson, K.,R. and Rodrigo, A. G. (2009). A statistical model to identify differentially expressed proteins in 2D PAGE gels. PLoS Computational Biology 5(9): e1000509.
Rodrigo, A. G., Goracke, P. C., Rowhanian, K., and Mullins, J. I. (1997). Quantitation of target molecules from PCR-based limiting dilution assays. AIDS Research and Human
Evolutionary analyses using short-read sequences derived from pooled samples of rapidly evolving viruses
New sequencing technologies that generate large numbers of short reads challenge the evolutionary analyses of samples of rapidly evolving viruses (REVs) because information is literally fragmented. We propose to develop statistical methods, specifically computational Bayesian Markov chain Monte Carlo estimation, that will take account of the uncertainty of sequence reassembly in the estimation of evolutionary parameters and trees.
Ecological and Evolutionary Dynamics of Microbiomes
We’ve just started working on this. We are trying to develop an explicit framework that will allow us to model the microbial composition within hosts, by taking account of the ecological and evolutionary processes that influence how hosts acquire microbes and how microbes survive in hosts. This is a model-based research project, and we are working on our first paper.
Evolution of Cancer Cells
This is another project that is completely new to the lab. We are interested in knowing what the genealogies of samples of cells from tumors can tell us about the likely clinical outcomes of cancers. Again, we are taking a modelling approach, but we will be working with Ned Patz on this.