title: Phylogenetic signal and noise: predicting the power of a data set to resolve phylogeny.
abstract: A principal objective for phylogenetic experimental design is to predict the power of a data set to resolve nodes in a phylogenetic tree. However, proactively assessing the potential for phylogenetic noise compared with signal in a candidate data set has been a formidable challenge. Understanding the impact of collection of additional sequence data to resolve recalcitrant internodes at diverse historical times will facilitate increasingly accurate and cost-effective phylogenetic research. Here, we derive theory based on the fundamental unit of the phylogenetic tree, the quartet, that applies estimates of the state space and the rates of evolution of characters in a data set to predict phylogenetic signal and phylogenetic noise and therefore to predict the power to resolve internodes. We develop and implement a Monte Carlo approach to estimating power to resolve as well as deriving a nearly equivalent faster deterministic calculation. These approaches are applied to describe the distribution of potential signal, polytomy, or noise for two example data sets, one recent (cytochrome c oxidase I and 28S ribosomal rRNA sequences from Diplazontinae parasitoid wasps) and one deep (eight nuclear genes and a phylogenomic sequence for diverse microbial eukaryotes including Stramenopiles, Alveolata, and Rhizaria). The predicted power of resolution for the loci analyzed is consistent with the historic use of the genes in phylogenetics.
authors.affiliation: Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA. Jeffrey.Townsend@Yale.edu
authors.forename: Jeffrey P
authors.initials: JP
authors.lastname: Townsend
authors.forename: Zhuo
authors.initials: Z
authors.lastname: Su
authors.forename: Yonas I
authors.initials: YI
authors.lastname: Tekle
mesh_terms: D056893:Alveolata / Q000145:classification / Q000235:genetics*; D000818:Animals; D002467:Cell Nucleus / Q000235:genetics; D002965:Classification / Q000379:methods*; D003576:Electron Transport Complex IV / Q000235:genetics; D017344:Genes, Insect; D019476:Insect Proteins / Q000235:genetics; D009010:Monte Carlo Method; D010802:Phylogeny; D011506:Proteins / Q000235:genetics; D012339:RNA, Ribosomal, 28S / Q000235:genetics; D012269:Ribosomal Proteins / Q000235:genetics; D017422:Sequence Analysis, DNA; D014863:Wasps / Q000145:classification / Q000235:genetics*
chemical_list: D019476:Insect Proteins; D011506:Proteins; D012339:RNA, Ribosomal, 28S; D012269:Ribosomal Proteins; D003576:Electron Transport Complex IV