CherryML: Scalable Maximum Likelihood Estimation of Phylogenetic Models

Abstract

AbstractPhylogenetic models of molecular evolution are central to diverse problems in biology, but maximum likelihood estimation of model parameters is a computationally expensive task, in some cases prohibitively so. To address this challenge, we here introduce CherryML, a broadly applicable method that achieves several orders of magnitude speedup. We demonstrate its utility by applying it to estimate a general 400 × 400 rate matrix for amino acid co-evolution at protein contact sites.