MOTIVATION: 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. RESULTS: 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 (i) parallelization of quartet likelihood calculations during composite likelihood computation, (ii) weighted random selection of quartets, and (iii) 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. AVAILABILITY AND IMPLEMENTATION: SNaQ.jl is a new open source Julia package available at https://github.com/JuliaPhylo/SNaQ.jl.

Semidirected networks have received interest in evolutionary biology as the appropriate generalization of unrooted trees to networks, in which some but not all edges are directed. Yet these networks lack proper theoretical study. We define here a general class of semidirected phylogenetic networks, with a stable set of leaves, tree nodes and hybrid nodes. We prove that for these networks, if we locally choose the direction of one edge, then globally the set of directed paths starting at this edge is stable across all choices to root the network. We define an edge-based representation of semidirected phylogenetic networks and use it to define a dissimilarity between networks, which can be efficiently computed in near-quadratic time. Our dissimilarity extends the widely-used Robinson-Foulds distance on both rooted trees and unrooted trees. After generalizing the notion of tree-child networks to semidirected networks, we prove that our edge-based dissimilarity is in fact a distance on the space of tree-child semidirected phylogenetic networks.

MOTIVATION: Kinship estimation is necessary for evaluating violations of assumptions or testing certain hypotheses in many population genomic studies. However, kinship estimators are usually designed for diploid systems and cannot be used in populations with mixed haploid diploid genetic systems. The only estimators for different ploidies require datasets free of population structure, limiting their usage. RESULTS: We present KIMGENS (Kinship Inference for Mixed GENetic Systems), an estimator for kinship estimation among individuals of various ploidies, that is robust to population structure. This estimator is based on the popular KING-robust estimator but uses diploid relatives of the individuals of interest as references of heterozygosity and extends its use to haploid-diploid and haploid pairs of individuals. We demonstrate that KIMGENS estimates kinship more accurately than previously developed estimators in simulated panmictic, structured and admixed populations, but has lower accuracy when the individual of interest is inbred. KIMGENS also outperforms other estimators in a honeybee dataset. Therefore, KIMGENS is a valuable addition to a population geneticist's toolbox. AVAILABILITY AND IMPLEMENTATION: KIMGENS and its association simulation tool are implemented and available open-source at https://github.com/YenWenWang/HapDipKinship. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: With growing genome-wide molecular datasets from next-generation sequencing, phylogenetic networks can be estimated using a variety of approaches. These phylogenetic networks include events like hybridization, gene flow or horizontal gene transfer explicitly. However, the most accurate network inference methods are computationally heavy. Methods that scale to larger datasets do not calculate a full likelihood, such that traditional likelihood-based tools for model selection are not applicable to decide how many past hybridization events best fit the data. We propose here a goodness-of-fit test to quantify the fit between data observed from genome-wide multi-locus data, and patterns expected under the multi-species coalescent model on a candidate phylogenetic network. RESULTS: We identified weaknesses in the previously proposed TICR test, and proposed corrections. The performance of our new test was validated by simulations on real-world phylogenetic networks. Our test provides one of the first rigorous tools for model selection, to select the adequate network complexity for the data at hand. The test can also work for identifying poorly inferred areas on a network. AVAILABILITY AND IMPLEMENTATION: Software for the goodness-of-fit test is available as a Julia package at https://github.com/cecileane/QuartetNetworkGoodnessFit.jl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Motivation: BUCKy is a C++ program that implements Bayesian concordance analysis. The method uses a non-parametric clustering of genes with compatible trees, and reconstructs the primary concordance tree from clades supported by the largest proportions of genes. A population tree with branch lengths in coalescent units is estimated from quartet concordance factors. Availability: BUCKy is open source and distributed under the GNU general public license at www.stat.wisc.edu/∼ane/bucky/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.