Gene tree distributions under the coalescent process.

Under the coalescent model for population divergence, lineage sorting can cause considerable variability in gene trees generated from any given species tree. In this paper, we derive a method for computing the distribution of gene tree topologies given a bifurcating species tree for trees with an arbitrary number of taxa in the case that there is one gene sampled per species. Applications for gene tree distributions include determining exact probabilities of topological equivalence between gene trees and species trees and inferring species trees from multiple datasets.

A comparison of methods for estimating the transition:transversion ratio from DNA sequences.

Estimation of the ratio of the rates of transitions to transversions (TI:TV ratio) for a collection of aligned nucleotide sequences is important because it provides insight into the process of molecular evolution and because such estimates may be used to further model the evolutionary process for the sequences under consideration. In this paper, we compare several methods for estimating the TI:TV ratio, including the pairwise method [TREE 11 (1996) 158], a modification of the pairwise method due to Ina [J. Mol.

A statistical framework for combining and interpreting proteomic datasets.

Motivation: To identify accurately protein function on a proteome-wide scale requires integrating data within and between high-throughput experiments. High-throughput proteomic datasets often have high rates of errors and thus yield incomplete and contradictory information. In this study, we develop a simple statistical framework using Bayes' law to interpret such data and combine information from different high-throughput experiments.

Estimation of evolutionary parameters with phylogenetic trees.

An important issue in the phylogenetic analysis of nucleotide sequence data using the maximum likelihood (ML) method is the underlying evolutionary model employed. We consider the problem of simultaneously estimating the tree topology and the parameters in the underlying substitution model and of obtaining estimates of the standard errors of these parameter estimates. Given a fixed tree topology and corresponding set of branch lengths, the ML estimates of standard evolutionary model parameters are asymptotically efficient, in the sense that their joint distribution is asymptotically normal with the variance-covariance matrix given by the inverse of the Fisher information matrix.