Improving phylogenetic analyses by incorporating additional information from genetic sequence databases.

ABSTRACT:

Motivation

Statistical analyses of phylogenetic data culminate in uncertain estimates of underlying model parameters. Lack of additional data hinders the ability to reduce this uncertainty, as the original phylogenetic dataset is often complete, containing the entire gene or genome information available for the given set of taxa. Informative priors in a Bayesian analysis can reduce posterior uncertainty; however, publicly available phylogenetic software specifies vague priors for model parameters by default. We build objective and informative priors using hierarchical random effect models that combine additional datasets whose parameters are not of direct interest but are similar to the analysis of interest.

Results

We propose principled statistical methods that permit more

SUBMITTER: Liang LJ 

PROVIDER: S-EPMC2800350 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature