Showing papers by "Bret Larget published in 2005"
TL;DR: The purpose of this article is to quantify the uncertainty among the relationships of metazoan phyla on the basis of mitochondrial genome arrangements while incorporating prior knowledge of the monophyly of various groups from other sources.
Abstract: Genome arrangements are a potentially powerful source of information to infer evolutionary relationships among distantly related taxa. Mitochondrial genome arrangements may be especially informative about metazoan evolutionary relationships because (1) nearly all animals have the same set of definitively homologous mitochondrial genes, (2) mitochondrial genome rearrangement events are rare relative to changes in sequences, and (3) the number of possible mitochondrial genome arrangements is huge, making convergent evolution of genome arrangements appear highly unlikely. In previous studies, phylogenetic evidence in genome arrangement data is nearly always used in a qualitative fashion-the support in favor of clades with similar or identical genome arrangements is considered to be quite strong, but is not quantified. The purpose of this article is to quantify the uncertainty among the relationships of metazoan phyla on the basis of mitochondrial genome arrangements while incorporating prior knowledge of the monophyly of various groups from other sources. The work we present here differs from our previous work in the statistics literature in that (1) we incorporate prior information on classifications of metazoans at the phylum level, (2) we describe several advances in our computational approach, and (3) we analyze a much larger data set (87 taxa) that consists of each unique, complete mitochondrial genome arrangement with a full complement of 37 genes that were present in the NCBI (National Center for Biotechnology Information) database at a recent date. In addition, we analyze a subset of 28 of these 87 taxa for which the non-tRNA mitochondrial genomes are unique where the assumption of our inversion-only model of rearrangement is more plausible. We present summaries of Bayesian posterior distributions of tree topology on the basis of these two data sets.
71 citations
TL;DR: Assessment of the robustness of posterior distributions to different specifications of prior distributions and an empirical means to selecting a prior distribution to compute probability distributions of ancestral genome arrangements are assessed.
39 citations
TL;DR: This is an electronic version of an article published in Systematic Biology, where the Hastings ratio of the local proposal used in Bayesian phylogenetics was compared to the standard deviation.
Abstract: This is an electronic version of an article published in Systematic Biology ["Holder, Mark T., Paul O. Lewis, David L. Swofford, and Bret Larget. Hastings ratio
of the local proposal used in Bayesian phylogenetics. Systematic Biology, 54:961{965, 2005.] Systematic Biology is available online at informaworld http://dx.doi.org/10.1080/10635150500354670
31 citations
01 Jan 2005
TL;DR: Markov chain Monte Carlo has proved to be highly useful because of its great flexibility and its success at solving many high-dimensional integration problems where other methods are computationally prohibitive.
Abstract: Markov chain Monte Carlo (MCMC) is a general computational technique for evaluating sums and integrals, especially those that arise as probabilities or expectations under complex probability distributions. Monte Carlo implies that the method is based on using chance (in the form of a pseudo-random number generator). Markov chain indicates a dependent sampling scheme with the probability distribution of each sampled point depending on the value of the previous one. Due to this dependence, MCMC samplers typically require sample sizes that are substantially larger than the sizes of independent samples produced by Monte Carlo integration methods to be able to achieve similar accuracy. However, independent sampling methods often require detailed knowledge of characteristics of the function being integrated, as their computational efficiency relies upon having a close approximation of this function. MCMC has proved to be highly useful because of its great flexibility and its success at solving many high-dimensional integration problems where other methods are computationally prohibitive.
18 citations