Measuring the relative contribution to predictive power of modern nucleotide substitution modeling approaches

Author:

Bujaki Thomas12,Van Looyen Katharine1,Rodrigue Nicolas123

Affiliation:

1. Department of Biology, Carleton University , Ontario K1S 5B6, Canada

2. Institute of Biochemistry, Carleton University , Ontario K1S 5B6, Canada

3. School of Mathematics and Statistics, Carleton University , Ontario K1S 5B6, Canada

Abstract

SummaryTraditional approaches to probabilistic phylogenetic inference have relied on information-theoretic criteria to select among a relatively small set of substitution models. These model selection criteria have recently been called into question when applied to richer models, including models that invoke mixtures of nucleotide frequency profiles. At the nucleotide level, we are therefore left without a clear picture of mixture models’ contribution to overall predictive power relative to other modeling approaches. Here, we utilize a Bayesian cross-validation method to directly measure the predictive performance of a wide range of nucleotide substitution models. We compare the relative contributions of free nucleotide exchangeability parameters, gamma-distributed rates across sites, and mixtures of nucleotide frequencies with both finite and infinite mixture frameworks. We find that the most important contributor to a model’s predictive power is the use of a sufficiently rich mixture of nucleotide frequencies. These results suggest that mixture models should be given greater consideration in nucleotide-level phylogenetic inference.

Funder

Natural Sciences and Engineering Council of Canada and Carleton University

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Genetics,Molecular Biology,Structural Biology

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