Comparing the topology of phylogenetic network generators

Abstract

Phylogenetic networks represent evolutionary history of species and can record natural reticulate evolutionary processes such as horizontal gene transfer and gene recombination. This makes phylogenetic networks a more comprehensive representation of evolutionary history compared to phylogenetic trees. Stochastic processes for generating random trees or networks are important tools in evolutionary analysis, especially in phylogeny reconstruction where they can be utilized for validation or serve as priors for Bayesian methods. However, as more network generators are developed, there is a lack of discussion or comparison for different generators. To bridge this gap, we compare a set of phylogenetic network generators by profiling topological summary statistics of the generated networks over the number of reticulations and comparing the topological profiles.

A polynomial invariant for a new class of phylogenetic networks

“…In this paper a new polynomial invariant for (binary) phylogenetic networks is introduced. This generalises results in both [LBGC20] for phylogenetic trees and in [JL21] for phylogenetic networks where their set of embedded spanning trees (like tree-child) characterizes it. The polynomial presented here is an invariant for general phylogenetic networks; it is not exclusive to specific subclasses of phylogenetic networks, as common with other invariants.…”

“…Those trees characterize tree-child phylogenetic networks [FM18], but not general ones. In [JL21], the Liu polynomial is generalized to phylogenetic networks by their sets of embedded spanning trees. Roughly speaking, the polynomial of the network is the product of the polynomials of the embedded spanning trees (considering trees with multiplicity).…”

“…Motivated to analyze and compare tree shapes in a phylogenetic context, this polynomial (which we will refer to as the Liu polynomial ) has been used both to define a similarity measure on rooted tree shapes and to estimate parameters and models via their coefficients [LBGC20]. Moreover, its generalization from trees to networks (by analyzing the set of embedded spanning trees in the network) has also been used to study the properties of randomly generated networks [JL21].…”