Generalised Bayesian distance-based phylogenetics for the genomics era

• DOI: 10.48550/arxiv.2502.04067
• Publication Date: 2025-02-06
• Authors: Matthew J. Penn; Neil Scheidwasser...; Mark P. Khurana; Christl A. Donnelly; David A. Duchêne; Samir Bhatt
• Keywords: FOS: Biological sciences; Populations and Evolution (q-bio.PE); FOS: Mathematics; Mathematics - Statistics Theory; Statistics Theory (math.ST); Quantitative Biology - Populations and Evolution

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