Build a better bootstrap and the RAWR shall beat a random path to your door: phylogenetic support estimation revisited

Abstract

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. 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. RAWR’s 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. We also conduct a re-analysis of large-scale genomic sequence data from a recent study of Darwin’s finches. Our findings clarify phylogenetic uncertainty in a charismatic clade that serves as an important model for complex adaptive evolution. Availability and implementation 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.

An empirical test of the relationship between the bootstrap and likelihood ratio support in maximum likelihood phylogenetic analysis

“…All of the papers that used the ML criterion used BS to measure support (Table ). Furthermore, not only is BS most widely used, but there continues to be an influx of new algorithms to improve BS analysis (e.g., Chang et al., 2019; Chatzou et al., 2018; Klötzl & Haubold, 2016; Lemoine et al., 2018; Wang & Liu, 2020; Wang et al., 2016, 2020).…”