Professor of Botany and Statistics
241 Birge Hall
Ph.D. (1994) University of California, Berkeley
Bayesian modeling of molecular evolution; develops statistical methods to analyze genetic data in order to estimate phylogenetic trees with expressions of uncertainty
My research focuses on the development of statistical methods to analyze genetic data in order to estimate phylogenetic trees while accounting for uncertainty. I am particularly interested in developing models for the evolution of DNA that incorporate current understanding of important biological processes but are feasible for statistical analysis. The Bayesian approach to statistical phylogenetics is particularly appealing, because probability is a natural and easily understood way to express uncertainty in estimates and strength of belief in various hypotheses. The computational approach of Markov chain Monte Carlo allows for practical Bayesian analysis with complicated and realistic models.
The software Bayesian Analysis in Molecular Biology and Evolution (BAMBE), developed with Donald Simon at Duquesne University is a free software package for estimation of phylogenetic trees on the basis of aligned DNA sequence data. Recently, we have developed methods for the analysis of genome arrangement data, which we will distribute eventually as new software in BAMBE. A current research activity is the development of methods to combine data of several different types along with prior information in a single Bayesian analysis.
I will teach this fall an introductory course in statistics for undergraduate biology majors. Details on this course and others are at my Statistics Department web page.
Huelsenbeck, J., B. Rannala, and B. Larget (to appear). A statistical perspective for reconstructing the history of host-parasite associations. In Page, R (Ed.),Tangled Trees: Phylogenies, Cospeciation, and Coevolution. The University of Chicago Press.
Larget, B., D. L. Simon, and J.B. Kadane (to appear with discussion). On a Bayesian approach to phylogenetic inference from animal mitochondrial genome arrangements. Journal of the Royal Statistical Society B.
Simon, D. L. and B. Larget (2001). Phylogenetic inference from mitochondrial genome arrangement data. In Alexandrov, V. N., J. Dongarra, B. Juliano, R. Renner, C. Tan (Ed.), Computational Science - ICCS 2001. Springer-Verlag Lecture Notes in Computer Science, 2074: 1022-1028.
Mizanur Rahman, G. M., T. L. Isenhour, B. Larget, and P. D. Greenlaw (2001). Statistical Analysis of DOE EML QAP Data from 1982 to 1998. Journal of Chemical Information and Computer Sciences 41 1099-1105.
Huo, D., S. Kingston, and B. Larget (2000). Application of isotope dilution in elemental speciation: speciated isotope dilution mass spectrometry (SIDMS). In Caruso, J., K.L. Sutton, K.L. Ackley (Ed.), Elemental Speciation, New Approaches for Trace Element Analysis. Elsevier Comprehensive Analytical Chemistry, XXXIII, 277-313.
Huelsenbeck, J., B. Larget, and D. Swofford (2000). A compound Poisson process for relaxing the molecular clock. Genetics 154:1879-1892.
Huelsenbeck, J., B. Rannala, and B. Larget (2000). A Bayesian framework for the analysis of cospeciation. Evolution 54(2):353-364.
Larget, B. and D. Simon (1999). Markov chain Monte Carlo algorithms for the Bayesian analysis of phylogenetic trees. Molecular Biology and Evolution 16:750-759.
Mau, B., M.A. Newton, and B. Larget (1999). Bayesian phylogenetic inference via Markov Chain Monte Carlo methods. Biometrics 55:1-12.
Newton, M.A., B. Mau, and B. Larget (1999). Markov chain Monte Carlo for the Bayesian analysis of evolutionary trees from aligned molecular sequences. In F. Seillier-Moseiwitch (Ed.), Statistics in Molecular Biology and Genetics. IMS Lecture Notes-Monograph Series, 33, 143-162.
Larget, B. (1998). A Canonical Representation for Hidden Markov Models. Journal of Applied Probability 35:313-324.
Aldous, D. and B. Larget (1992). A tree-based scaling exponent for random cluster models. Journal of Physics A: Mathematical and General 25 L1065 - L1069.