Build a Better Bootstrap and the RAWR Shall Beat a Random Path to Your Door: Phylogenetic Support Estimation Revisited

Motivation: The standard bootstrap method is used throughout science and engineering to perform general-purpose non-parametric resampling and re-estimation. Among the most widely cited and widely used such applications is the phylogenetic bootstrap method, which Felsenstein proposed in 1985 as a means to place statistical confidence intervals on an estimated phylogeny (or estimate "phylogenetic support"). A key simplifying assumption of the bootstrap method is that input data are independent and identically distributed (i.i.d.). However, the i.i.d. assumption is an over-simplification for biomolecular sequence analysis, as Felsenstein noted. Special-purpose fully parametric or semi-parametric methods for phylogenetic support estimation have since been introduced, some of which are intended to address this concern. Results: In this study, we introduce a new sequence-aware non-parametric resampling technique, which we refer to as RAWR ("RAndom Walk Resampling"). RAWR consists of random walks that synthesize and extend the standard bootstrap method and the "mirrored inputs" idea of Landan and Graur. We apply RAWR to the task of phylogenetic support estimation. RAWR9s performance is compared to the state of the art using synthetic and empirical data that span a range of dataset sizes and evolutionary divergence. We show that RAWR support estimates offer comparable or typically superior type I and type II error compared to phylogenetic bootstrap support as well as GUIDANCE2, a state-of-the-art purpose-built fully parametric method. Additional simulation study experiments help to clarify practical considerations regarding RAWR support estimation. We conclude with thoughts on future research directions and the untapped potential for sequence-aware non-parametric resampling and re-estimation. Availability: Data and software are publicly available under open-source software and open data licenses at: https://gitlab.msu.edu/liulab/RAWR-study-datasets-and-scripts.