Biology subjects

Kolbow, N.

Publications and source records attributed to Kolbow, N..

3 recordsLinked to original sources

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↗

bioRxiv2025

Sparse learning for scalable phylogenetic network inference

Phylogenetic networks account for signals of hybridization, reticulation, and gene flow, and provide an opportunity to analyse species evolution from a more complex perspective than bifurcating phylogenetic trees. However, even the fastest algorithms for inferring these networks face scalability challenges as the number of species increases. This limitation arises because these methods use as input a large concordance factors (CFs) table, which summarizes the observed CFs of all possible four-species combinations in each row. The size of this table scales with the fourth power of the number of species, creating computational bottlenecks and highlighting the need for more efficient solutions. Sparse learning has been shown to reduce the dimensionality of large-scale datasets while producing results of comparable quality to those obtained using the full dataset. In this study, we adapted two sparse machine learning models--Elastic Net and Ensemble Learning + Elastic Net--to guide the subsample of an optimal number of rows from the CFs table required to accurately predict the overall phylogenetic network pseudolikelihood. Both methods account for the inherent correlation among rows, which arises because rows overlap in species information. We call this method Qsin. In two simulated datasets, Qsin reduced the dataset by approximately half without compromising accuracy. For the Xiphophorus fishes dataset, which contains 10,626 rows in the CFs table, we recovered the same topology as with the full CFs table but using only 763 rows. Using these subsamples also reduced running times by up to 60% without compromising accuracy. These gains are expected to persist as species numbers increase. Qsin contributes to ongoing efforts to make phylogenetic network inference more efficient and opens the door to analyses of more complex evolutionary histories. The source code for Qsin is freely available at: https://github.com/ulises-rosas/qsin.

evolutionary biology↗

bioRxiv2025

A method for massively scalable phylogenetic network inference

Recent advances in sequencing technologies have enabled large-scale phylogenomic analyses. While these analyses often rely on phylogenetic trees, increasing evidence suggests that non-treelike evolutionary events, such as hybridization and horizontal gene transfer, are prevalent in the evolutionary histories of many species, in which case tree-based models are insufficient. Phylogenetic networks can capture such complex evolutionary histories, but current methods for accurately inferring them lack scalability. Implicit network inference methods are fast but lack biological interpretability. Here, we introduce a novel method called InPhyNet that merges a set of non-overlapping, independently inferred level-1 networks into a unified topology, achieving linear scalability while maintaining high accuracy under the multispecies network coalescent model. We prove that a statistically consistent and scalable pipeline for inferring phylogenetic networks can be constructed with InPhyNet. Using simulation, we infer networks with up to 200 taxa and show that divide-and-conquer pipelines utilizing InPhyNet allow for accurate network inference at scales and speeds previously unseen. Re-analyzing a phylogeny of 1,158 land plants with InPhyNet, we recover well-accepted reticulate events and illustrate how InPhyNet enables large-scale analyses of biologically meaningful reticulate phylogenies at previously unprecedented scales.

evolutionary biology↗