Split scores: a tool to quantify phylogenetic signal in genome-scale data

TL;DR

The paper introduces the split score, a rapid and algebraic method to quantify phylogenetic support and detect evolutionary process variation along genomes using genome-scale data, applicable to large datasets.

Contribution

It presents the split score as a novel, efficient tool for assessing phylogenetic support and detecting evolutionary shifts in genome-scale data, outperforming traditional likelihood-based methods.

Findings

Split score effectively detects true phylogenetic splits.

It can identify changes in evolutionary processes across genomes.

The method is computationally efficient for large datasets.

Abstract

Detecting variation in the evolutionary process along chromosomes is increasingly important as whole-genome data becomes more widely available. For example, factors such as incomplete lineage sorting, horizontal gene transfer, and chromosomal inversion are expected to result in changes in the underlying gene trees along a chromosome, while changes in selective pressure and mutational rates for different genomic regions may lead to shifts in the underlying mutational process. We propose the split score as a general method for quantifying support for a particular phylogenetic relationship within a genomic data set. Because the split score is based on algebraic properties of a matrix of site pattern frequencies, it can be rapidly computed, even for data sets that are large in the number of taxa and/or in the length of the alignment, providing an advantage over other methods (e.g., maximum likelihood) that are often used to assess such support. Using simulation we explore the properties of the split score, including its dependence on sequence length, branch length, size of a split and its ability to detect true splits in the underlying tree. Using a sliding window analysis, we show that split scores can be used to detect changes in the underlying evolutionary process for genome-scale data from primates, mosquitoes, and viruses in a computationally efficient manner. Computation of the split score has been implemented in the software package SplitSup.