Resampling Residuals: Robust Estimators of Error and Fit for Evolutionary Trees and Phylogenomics

TL;DR

This paper introduces a resampling residuals method for robustly estimating error and confidence in evolutionary trees, effectively capturing both stochastic and systematic uncertainties in phylogenomics.

Contribution

It presents a novel residual resampling approach for phylogenetic error estimation, extending to fast BME methods, improving uncertainty assessment in genome-scale analyses.

Findings

Residual resampling closely matches traditional bootstrap results.

Method reveals underestimated uncertainties in real data.

Applicable to fast BME criterion with promising results.

Abstract

Phylogenomics, even more so than traditional phylogenetics, needs to represent the uncertainty in evolutionary trees due to systematic error. Here we illustrate the analysis of genome-scale alignments of yeast, using robust measures of the additivity of the fit of distances to tree when using flexi Weighted Least Squares. A variety of DNA and protein distances are used. We explore the nature of the residuals, standardize them, and then create replicate data sets by resampling these residuals. Under the model, the results are shown to be very similar to the conventional sequence bootstrap. With real data they show up uncertainty in the tree that is either due to underestimating the stochastic error (hence massively overestimating the effective sequence length) and/or systematic error. The methods are extended to the very fast BME criterion with similarly promising results.