Quantifying Reticulation in Phylogenetic Complexes Using Homology

TL;DR

This paper introduces a topological data analysis approach to detect and quantify reticulate evolution in phylogenetic data, addressing limitations of traditional tree-based models and proposing new methods for analysis.

Contribution

It presents an extension of homology-based methods, including the median complex, to better detect reticulation in phylogenetic datasets.

Findings

Higher homology correlates with reticulate evolution

Standard filtration may miss reticulation signals

Median complex improves detection of reticulate events

Abstract

Reticulate evolutionary processes result in phylogenetic histories that cannot be modeled using a tree topology. Here, we apply methods from topological data analysis to molecular sequence data with reticulations. Using a simple example, we demonstrate the correspondence between nontrivial higher homology and reticulate evolution. We discuss the sensitivity of the standard filtration and show cases where reticulate evolution can fail to be detected. We introduce an extension of the standard framework and define the median complex as a construction to recover signal of the frequency and scale of reticulate evolution by inferring and imputing putative ancestral states. Finally, we apply our methods to two datasets from phylogenetics. Our work expands on earlier ideas of using topology to extract important evolutionary features from genomic data.