Biology subjects

Justison, J.

Publications and source records attributed to Justison, J..

4 recordsLinked to original sources

bioRxiv2026

Beyond Level-1: Fast Inference of Generic Semi-directed Phylogenetic Networks

Hybridization, introgression, and lateral gene transfer shape genomes across the diversity of life, but our ability to reconstruct these histories has been constrained by a methodological bottleneck: nearly all network inference methods are restricted to level-1 topologies, leaving more complex reticulate histories methodologically inaccessible. Here, we extend the widely used SNaQ method to scalably infer arbitrary binary, metric, semi-directed phylogenetic networks. Computational improvements yield substantial speedups in marginal composite likelihood evaluation, enabling genome-scale network inference under this framework for the first time. We systematically evaluate inference accuracy across networks of varying complexity, including cases where the true history falls outside the inferred search space, and find that SNaQ reliably recovers complex reticulate histories under diverse conditions, and still recovers meaningful information about hybridization events even when the full topology is not correctly inferred. Applied to the phylogeny of Xiphophorus (Poeciliidae), the method reveals a richer history of hybridization than level-1 approaches could capture, with network models that fit the data significantly better than previously inferred topologies. By enabling scalable inference beyond level-1 networks, our work facilitates the reconstruction of far richer reticulate histories from genomic data, bringing phylogenetic analysis closer to capturing the full network of life.

evolutionary biology↗

bioRxiv2025

SNaQ.jl: Improved Scalability for Phylogenetic Network Inference

Phylogenetic networks represent complex biological scenarios that are overlooked in trees, such as hybridization and horizontal gene transfer. Although numerous methods have been developed for phylogenetic network inference, their scalability is severely limited by the computational demands of likelihood optimization and the vastness of network space. Composite (or pseudo-) likelihood approaches like SNaQ have improved computational tractability for network inference, but they remain inadequate for datasets of sizes routinely handled by tree inference methods. Here, we introduce SNaQ.jl, a new standalone Julia package with the composite likelihood inference originally implemented within PhyloNetworks.jl as well as new scalability features that enhance computational efficiency through (1) parallelization of quartet likelihood calculations during composite likelihood computation, (2) weighted random selection of quartets, and (3) probabilistic decision-making during network search. Through a simulation study and empirical data analysis, we show that this new version of SNaQ.jl (version 1.1) improves average runtimes by up to 499% on average with no change in function parameters or method accuracy.

evolutionary biology↗

bioRxiv2022

Exploring the distribution of phylogenetic networks generated under a birth-death-hybridization process

Gene-flow processes such as hybridization and introgression play important roles in shaping diversity across the tree of life. Recent studies extending birth-death models have made it possible to investigate patterns of reticulation in a macroevolutionary context. These models allow for different macroevolutionary patterns of gene flow events that can either add, maintain, or remove lineages--with the gene flow itself possibly being dependent on the relatedness between species--thus creating complex diversification scenarios. Further, many reticulate phylogenetic inference methods assume specific reticulation structures or phylogenies belonging to certain network classes. However, the distributions of phylogenetic networks under reticulate birth-death processes are poorly characterized, and it is unknown whether they violate common methodological assumptions. We use simulation techniques to explore phylogenetic network space under a birth-death-hybridization process where the hybridization rate can have a linear dependence on genetic distance. Specifically, we measured the number of lineages through time and role of hybridization in diversification along with the proportion of phylogenetic networks that belong to commonly used network classes (e.g., tree-child, tree-based, or level-1 networks). We find that the growth of phylogenetic networks and class membership are largely affected by assumptions about macroevolutionary patterns of gene flow. In accordance with previous studies, a lower proportion of networks belonged to these classes based on type and density of reticulate events. However, under a birth-death-hybridization process, these factors form an antagonistic relationship; the type of reticulation events that cause high membership proportions also lead to the highest reticulation density, consequently lowering the overall proportion of phylogenies in some classes. Further, we observed that genetic distance-dependent gene flow and incomplete sampling increase the proportion of class membership, primarily due to having fewer reticulate events. Our results can inform studies if their biological expectations of gene flow are associated with evolutionary histories that satisfy the assumptions of current methodology and aid in finding phylogenetic classes that are relevant for methods development.

evolutionary biology↗

bioRxiv2022

SiPhyNetwork: An R package for Simulating Phylogenetic Networks

O_LIGene flow is increasingly recognized as an important macroevolutionary process. The many mechanisms that contribute to gene flow (e.g., introgression, hybridization, lateral gene transfer) uniquely affect the diversification of dynamics of species, making it important to be able to account for these idiosyncrasies when constructing phylogenetic models. Existing phylogenetic-network simulators for macroevolution are limited in the ways they model gene flow. C_LIO_LIWe present SiPhyNetwork, an R package for simulating phylogenetic networks under a birth-death-hybridization process. C_LIO_LIOur package unifies the existing birth-death-hybridization models while also extending the toolkit for modeling gene flow. This tool can create patterns of reticulation such as hybridization, lateral gene transfer, and introgression. C_LIO_LISpecifically, we model different reticulate events by allowing events to either add, remove, or keep constant the number of lineages. Additionally, we allow reticulation events to be trait-dependent, creating the ability to model the expanse of isolating mechanisms that prevent gene flow. This tool makes it possible for researchers to model many of the complex biological factors associated with gene flow in a phylogenetic context. C_LI

evolutionary biology↗