Generalised Bayesian distance-based phylogenetics for the genomics era

TL;DR

This paper introduces a fast Bayesian phylogenetic method based on entropy-driven likelihood, enabling genome-scale analysis and revealing new insights into avian diversification with high computational efficiency.

Contribution

It extends distance-based phylogenetics with an entropy-based likelihood, bridging the gap with likelihood-based methods for efficient genome-scale Bayesian inference.

Findings

Method performs well on benchmark datasets

Reveals uncertainty in avian diversification post-K-Pg

Achieves high efficiency in large-scale genome analysis

Abstract

As whole genomes become widely available, maximum likelihood and Bayesian phylogenetic methods are demonstrating their limits in meeting the escalating computational demands. Conversely, distance-based phylogenetic methods are efficient, but are rarely favoured due to their inferior performance. Here, we extend distance-based phylogenetics using an entropy-based likelihood of the evolution among pairs of taxa, allowing for fast Bayesian inference in genome-scale datasets. We provide evidence of a close link between the inference criteria used in distance methods and Felsenstein's likelihood, such that the methods are expected to have comparable performance in practice. Using the entropic likelihood, we perform Bayesian inference on three phylogenetic benchmark datasets and find that estimates closely correspond with previous inferences. We also apply this rapid inference approach to a 60-million-site alignment from 363 avian taxa, covering most avian families. The method has outstanding performance and reveals substantial uncertainty in the avian diversification events immediately after the K-Pg transition event. The entropic likelihood allows for efficient Bayesian phylogenetic inference, accommodating the analysis demands of the genomic era.