Infinite Mixture Models for Improved Modeling of Across-Site Evolutionary Variation

TL;DR

This paper introduces flexible Bayesian infinite mixture models to better account for across-site evolutionary heterogeneity in phylogenetic analyses, improving model fit and scalability for large biological datasets.

Contribution

It presents novel infinite mixture modeling approaches, including hierarchical and hidden Markov models, integrated into BEAST X for enhanced phylogenetic inference.

Findings

Different models perform best in different viral datasets.

Models effectively infer the number of partitions and evolutionary parameters.

Efficient algorithms enable scaling to large datasets.

Abstract

Scientific studies in many areas of biology routinely employ evolutionary analyses based on the probabilistic inference of phylogenetic trees from molecular sequence data. Evolutionary processes that act at the molecular level are highly variable, and properly accounting for heterogeneity in evolutionary processes is crucial for more accurate phylogenetic inference. Nucleotide substitution rates and patterns are known to vary among sites in multiple sequence alignments, and such variation can be modeled by partitioning alignments into categories corresponding to different substitution models. Determining $\textit{a priori}$ appropriate partitions can be difficult, however, and better model fit can be achieved through flexible Bayesian infinite mixture models that simultaneously infer the number of partitions, the partition that each site belongs to, and the evolutionary parameters corresponding to each partition. Here, we consider several different types of infinite mixture models, including classic Dirichlet process mixtures, as well as novel approaches for modeling across-site evolutionary variation: hierarchical models for data with a natural group structure, and infinite hidden Markov models that account for spatial patterns in alignments. In analyses of several viral data sets, we find that different types of infinite mixture models emerge as the best choices in different scenarios. To enable these models to scale efficiently to large data sets, we adapt efficient Markov chain Monte Carlo algorithms and exploit opportunities for parallel computing. We implement this infinite mixture modeling framework in BEAST X, a widely-used software package for Bayesian phylogenetic inference.