Biology subjects

Solis-Lemus, C.

Publications and source records attributed to Solis-Lemus, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Sparse dynamic graphical models for longitudinal non-Gaussian data

Longitudinal non-Gaussian data are common in biomedical and microbiome studies, where responses may not be normally distributed. However, many existing dynamic graphical models are developed for Gaussian responses, while many non-Gaussian graphical models focus on cross-sectional dependence and do not explicitly estimate temporal transition effects. We propose a latent Gaussian dynamic graphical model for longitudinal non-Gaussian responses, including binary, count, and compositional outcomes. The proposed framework jointly estimates the contemporaneous conditional dependence network and the lag-one temporal dependence structure. We further develop a penalized Monte Carlo expectation maximization procedure for estimation. Simulation studies across multiple graph structures and outcome types show that the proposed method can recover both dependence structures, with more stable recovery for the lag-one temporal dependence structure and more structure-dependent recovery for the contemporaneous conditional dependence network. We apply the proposed method to longitudinal data from the Integrative Human Microbiome Project (HMP2) using a multinomial observation model. The results reveal persistent lag-one temporal dependence and contemporaneous conditional dependence among dominant microbial taxa, demonstrating the utility of the proposed framework for dynamic network analysis of longitudinal non-Gaussian biomedical data.

bioinformatics↗

bioRxiv2026

Assessing the robustness of SNaQ to violations induced by high-level phylogenetic networks

Phylogenetic networks extend the traditional tree model to capture reticulate evolutionary processes such as gene flow and hybridization. Among available inference tools, SNaQ is a widely used quartet-based method that offers a computationally efficient, statistically grounded approach to network estimation, but is limited to level-1 networks, in which reticulation cycles do not overlap. This assumption is a statistical requirement for identifiability rather than a reflection of biological reality, as many evolutionary scenarios, particularly those involving extensive or closely spaced gene flow, are expected to produce level-2 or higher networks. How SNaQ performs when this assumption is violated remains poorly understood. Here, we systematically evaluate SNaQs performance on simulated non-level-1 networks. Because existing network comparison metrics such as hardwired cluster dissimilarity are not true distances beyond level-1, we introduce complementary measures: hybrid cluster compatibility, blob compatibility, and tree-of-blobs comparison, to more directly assess structural recovery. We find that while SNaQ does not recover the exact topology of non-level-1 networks, it reliably infers the circular order of taxa and frequently recovers a tree of blobs compatible with the true network, suggesting the level-1 constraint acts as a form of regularization against overfitting. Recovery of reticulation signal is strongly tied to inheritance proportion and the user-specified maximum number of reticulations, with SNaQ behaving as a conservative estimator that favors strong, well-supported events over finer-scale or overlapping signals. These results clarify the strengths and limits of quartet-based network inference under model misspecification and offer practical guidance for applying SNaQ to complex reticulate histories.

evolutionary biology↗

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

Ancestral Sequences Cannot be Accurately Reconstructed via Interpolation in a Variational Autoencoder's Latent Space

Standard methods for ancestral sequence reconstruction (ASR) rely on substitution models for the residues in a biological sequence and assume independent evolution across these sites, ignoring the epistatic interactions that shape molecular evolution. In contrast, deep learning models like variational autoencoders (VAEs) can learn low-dimensional representations ("embeddings") of sequences in a protein family that may implicitly handle these dependencies, raising the possibility of performing more accurate ASR by interpolating between extant sequence embeddings within the VAE's latent space. In this study, we test this hypothesis by developing and evaluating a VAE-based ASR pipeline. Benchmarking this approach against established likelihood-based and parsimony methods using various simulations of protein evolution, including scenarios with and without epistasis, we find that the VAE-based approach is consistently and significantly outperformed by standard methods, even in epistatic regimes where it was hypothesized to have an advantage. We further show that this failure is not due to a lack of phylogenetic structure in the latent space, which does contain evolutionary signal. Rather, the primary limitation is the information loss inherent to the autoencoding process: the VAE's decoder cannot generate sequences with sufficient fidelity for the precise demands of ASR.

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↗

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

CMiNet: An R Package and User-Friendly Shiny App for Constructing Consensus Microbiome Networks

O_LIMicrobial networks offer critical insights into community structure, ecological interactions, and host-microbe dynamics. However, constructing reliable microbiome networks remains challenging due to variability among existing inference methods, limited overlap between networks, and the absence of a gold standard for validation. C_LIO_LIWe developed CMiNet (https://cminet.wid.wisc.edu), an interactive Shiny application and R package that enables consensus microbiome network construction by integrating up to ten widely used inference algorithms. CMiNet supports both correlation-based and conditional dependence-based methods and provides users with flexible options to construct individual or consensus networks across different approaches. C_LIO_LICMiNet employs a consensus-based filtering strategy that retains only edges supported by multiple methods, leading to more robust network structures that better reflect underlying biological interactions. This approach enhances reproducibility, minimizes method-specific biases, and improves biological interpretability. Its user-friendly interface allows researchers to upload data, customize network construction parameters, visualize networks interactively, and export results--without requiring programming expertise. C_LIO_LIWe demonstrated CMiNet using both gut and soil microbiome datasets. In the soil microbiome application, robust network construction enabled the consistent identification of disease-associated taxa. Specifically, we identified a set of key taxa, including Ktedonobacteria, Acidobacteriae, Vicinamibacteria, MB-A2-108, Planctomycetes, and Anaerolineae, selected by multiple independent methods. C_LI

systems 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↗

bioRxiv2025

Leveraging Bayesian Networks for Consensus Network Construction and Multi-Method Feature Selection to Decode Disease Prediction

Constructing reliable microbiome co-occurrence networks and identifying disease-associated taxa remain major challenges in microbiome research due to variability introduced by different inference algorithms. To overcome these limitations, we present CMIMN, a novel R package that uses a Bayesian network framework based on conditional mutual information to infer robust microbial interaction networks. To further enhance reliability, we construct a consensus microbiome network by integrating results from CMIMN and three widely used methods--SPIEC-EASI, SPRING, and SPARCC. This consensus approach, which overlays and weights edges shared across methods, reduces inconsistencies and provides a more biologically meaningful view of microbial relationships. In addition, we introduce a multi-method framework for identifying disease-associated microbial taxa by combining machine learning and network-based feature selection. Our ML pipeline applies distinct algorithms and identifies key taxa based on their consistent importance across models. Complementing this, we employ two network-based strategies that prioritize taxa based on centrality differences between clean tubers and scab-infected tubers networks, as well as a composite scoring system that ranks nodes using integrated network metrics. Our results show that CMIMN achieves high robustness in network inference, and that the consensus network further improves stability and interpretability. The multi-method feature selection framework enhances confidence in identifying biologically relevant taxa linked to potato common scab disease. Notably, we identify Bacteroidota, WPS-2, and Proteobacteria at the Phylum level; Actinobacteria, AD3, Bacilli, Anaerolineae, and Ktedonobacteria at the Class level; and C0119, Defluviicoccales, Bacteroidales, and Ktedonobacterales at the Order level as key taxa associated with disease status.

systems biology↗

bioRxiv2024

Unraveling Keystone Taxa: Interactions Within Microbial Networks and Environmental Dynamics in Lake Mendota

Microbial communities in freshwater ecosystems drive critical biogeochemical cycles, nutrient transformations, and energy flows essential for ecosystem stability. Yet, in the face of accelerating environmental changes, the responses of these microbial networks to spatial and temporal shifts remain underexplored, particularly with rising anoxia. We investigated the microbial ecosystems of Lake Mendota, Wisconsin, USA, through comprehensive metagenomic and metatranscriptomic analyses to elucidate their adaptations to environmental fluctuations across temporal and spatial dimensions. Employing tools like Sparse Inverse Covariance Estimation for Ecological Association Inference (SPIEC-EASI) and Conditional Auto-Regressive Least Absolute Shrinkage and Selection Operator (CARlasso), we identified key microbial taxa and their interactions with environmental parameters such as depth, temperature, pH, and dissolved oxygen. Our findings reveal that biological interactions more than environmental variables shape microbial community assembly and function. Specifically, keystone taxa from the phylum Bacteroidota emerged as pivotal in nutrient cycling and organic matter decomposition, processes crucial for sustaining water quality. Notably, these keystone taxa demonstrate dynamic adaptability, suggesting that microbial networks can rapidly adjust to changes in composition, a trait essential for resilience in the face of warming temperatures and altered precipitation patterns. This study provides critical insights into the resilience and adaptability of freshwater microbiomes, highlighting the role of microbial interactions in maintaining ecosystem health. By understanding how these microbial networks respond to environmental pressures, we can better predict shifts in microbial dynamics and anticipate the broader ecological impacts of climate change on freshwater systems. ImportanceThis research underscores the critical role of keystone taxa in freshwater ecosystems, highlighting how these organisms maintain water quality and contribute to the stability of aquatic environments. Understanding the ecological roles of these taxa is essential for developing strategies to manage ecosystems and conserve freshwater resources, particularly in the face of ongoing environmental challenges like climate change. The insights provided by this study not only enhance our comprehension of microbial interactions but also support effective ecosystem management and conservation efforts.

microbiology↗

bioRxiv2024

HighDimMixedModels.jl: Robust High Dimensional Mixed Models across Omics Data

High dimensional mixed-effect models are an increasingly important form of regression in modern biology, in which the number of variables often matches or exceeds the number of samples, which are collected in groups or clusters. The penalized likelihood approach to fitting these models relies on a coordinate gradient descent (CGD) algorithm that lacks guarantees of convergence to a global optimum. Here, we study empirically the behavior of the algorithm across a number of common study types in modern omics datatypes. In particular, we study the empirical performance of high dimensional mixed-effect models fit to data simulated to mimic the features of transcriptome, genome-wide association, and microbiome data. In addition, we study the performance of the model on real data from each of these study types. To facilitate these simulations, we implement the algorithm in an open source Julia package HighDimMixedModels.jl. We compare the performance of two commonly used penalties, namely LASSO and SCAD, within the HighDimMixedModels.jl framework. Our results demonstrate that the SCAD penalty consistently outperforms LASSO in terms of both variable selection and estimation accuracy across omics data. Through our comprehensive analysis, we illuminate the intricate relationship between algorithmic behavior, penalty selection, and dataset properties such as the correlation structure among features, providing valuable insights for researchers employing high dimensional mixed-effect models in biological investigations. Author SummaryHigh dimensional mixed-effect models are increasingly indispensable in modern biology, particularly in omics studies, where the number of variables often equals or surpasses the number of samples, and data are collected in clusters or groups. In our research, we concentrate on the penalized likelihood approach to fitting these models, employing a coordinate gradient descent (CGD) algorithm. While CGD is a widely used optimization technique, its convergence to a global optimum lack guarantees, prompting our empirical investigation into its behavior across various study types common in modern omics datasets. Our study provides insights into the performance of high dimensional mixed-effect models fitted to data simulating transcriptome, genome-wide association, and microbiome datasets. Additionally, we evaluate the models performance on real datasets from each of these study types. To facilitate reproducibility and further research, we have implemented the algorithm in an open-source Julia package, HighDimMixedModels.jl. Notably, HighDimMixedModels.jl stands out as the first package capable of seamlessly handling various omics datasets without errors, offering a user-friendly solution for researchers across disciplines. While numerous software packages are available for implementing high dimensional mixed-effects models on omics data, there is currently no comprehensive review source summarizing all methods. We provide a table summarizing existing methods, available in the Supplementary Material.

systems biology↗

bioRxiv2023

Extracting diamonds: Identifiability of 4-node cycles in level-1 phylogenetic networks under a pseudolikelihood coalescent model

Phylogenetic networks encode a broader picture of evolution by the inclusion of reticulate processes such as hybridization, introgression or horizontal gene transfer. Each reticulation event is represented by a "hybridization cycle". Here, we investigate the statistical identifiability of the position of the hybrid node in a 4-node hybridization cycle in a semi-directed level-1 phylogenetic network. That is, we investigate if our model is able to detect the correct placement of the hybrid node in the hybridization cycle from concordance factors as data. While generic identifiability is easily attained under non-restrictive assumptions such as t [isin] (0, {infty}) for all branches and{gamma} [isin] (0, 1) for the inheritance probability of the hybrid edges, simulations show that accurate detection of these cycles can be complicated by inadequate sampling, small sample size or gene tree estimation error. We identify practical advice for evolutionary biologists on best sampling strategies to improve the detection of this type of hybridization cycle.

evolutionary biology↗

bioRxiv2023

Non-linear phylogenetic regression using regularized kernels

O_LIPhylogenetic regression is a type of Generalized Least Squares (GLS) method that incorporates a covariance matrix based on the evolutionary relationships between species (i.e., phylogenetic relationships). While this method has found widespread use in hypothesis testing via comparative phylogenetic methods, such as phylogenetic ANOVA, its ability to account for non-linear relationships has received little attention. C_LIO_LITo address this issue, we utilized GLS in a high-dimensional feature space, employing linear combinations of transformed data to account for non-linearity, a common approach in kernel regression. We analyzed two biological datasets using both Radial Basis Function (RBF) and linear kernel transformations. The first dataset contained morphometric data, while the second dataset comprised discrete trait data and diversification rates as labels. Hyperparameter tuning of the model was achieved through cross-validation rounds in the training set. C_LIO_LIIn the tested biological datasets, regularized kernels reduced the error rate (as measured by RMSE) by around 20% compared to linear-based regression when data did not exhibit linear relationships. In simulated datasets, the error rate decreased almost exponentially with the level of non-linearity. C_LIO_LIThese results show that introducing kernels into phylogenetic regression analysis presents a novel and promising tool for complementing phylogenetic comparative methods. We have integrated this method into Python package named phyloKRR, which is freely available at: https://github.com/Ulises-Rosas/phylokrr. C_LI

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↗

bioRxiv2022

Machine learning identification of Pseudomonas aeruginosa strains from colony image data

When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical Pseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep learning. Using a train/validation/test split, our results achieve an average validation accuracy of 92.9% and an average test accuracy of 90.7% for the classification of individual strains. These results indicate that bacterial strains have characteristic visual fingerprints that can serve as the basis of classification on a sub-species level. Our work illustrates the potential of image-based classification of bacterial pathogens and highlights the potential to use similar approaches to predict medically relevant strain characteristics like antibiotic resistance and virulence from colony data. Author SummarySince the birth of microbiology, scientists have looked at the patterns of bacterial growth on agar (colony morphology) as a key tool for identifying bacterial species. We return to this traditional approach with modern tools of computer vision and deep learning and show that we can achieve high levels of classification accuracy on a within-species scale, despite what is considered a data-starved dataset. Our results show that strains of the environmental generalist and opportunistic pathogen Pseudomonas aeruginosa have a characteristic morphological fingerprint that enables accurate strain classification via a custom deep convolutional neural network. Our work points to extensions towards predicting phenotypes of interest (e.g. antibiotic resistance, virulence), and suggests that sample size limitations may be less restrictive than previously thought for deep learning applications in biology, given appropriate use of data augmentation and transfer-learning tools.

microbiology↗

bioRxiv2021

A Kernel Method for Dissecting Genetic Signals in Tests of High-Dimensional Phenotypes

Genomewide association studies increasingly employ multivariate tests of multiple correlated phenotypes to exploit likely pleiotropy to improve power. Typical multivariate methods produce a global p-value of association between a variant (or set of variants) and multiple phenotypes. When the global test is significant, subsequent interest then focuses on dissecting the signal and, in particular, delineating the set of phenotypes where the genetic variant(s) have a direct effect from the remaining phenotypes where the genetic variant(s) possess either indirect or no effect. While existing techniques like mediation models can be utilized for this purpose, they generally cannot handle high-dimensional phenotypic and genotypic data. To assist in filling this important gap, we propose a modification of a kernel distance-covariance framework for gene mapping of multiple variants with multiple phenotypes to test instead whether the association between the variants and a group of phenotypes is driven through a direct association with just a subset of the phenotypes. We use simulated data to show that our new method controls for type I error and is powerful to detect a variety of models demonstrating different patterns of direct and indirect effects. We further illustrate our method using GWAS data from the Grady Trauma Project and show that an existing signal between genetic variants in the ZHX2 gene and 21 items within the Beck Depression Inventory appears to be due to a direct effect of these variants on only 3 of these items. Our approach scales to genomewide analysis, and is applicable to high-dimensional correlated phenotypes.

genomics↗

bioRxiv2021

Efficient Estimation of Indirect Effects in Case-Control Studies Using a Unified Likelihood Framework

Mediation models are a set of statistical techniques that investigate the mechanisms that produce an observed relationship between an exposure variable and an outcome variable in order to deduce the extent to which the relationship is influenced by intermediate mediator variables. For a case-control study, the most common mediation analysis strategy employs a counterfactual framework that permits estimation of indirect and direct effects on the odds ratio scale for dichotomous outcomes, assuming either binary or continuous mediators. While this framework has become an important tool for mediation analysis, we demonstrate that we can embed this approach in a unified likelihood framework for mediation analysis in case-control studies that leverages more features of the data (in particular, the relationship between exposure and mediator) to improve efficiency of indirect effect estimates. One important feature of our likelihood approach is that it naturally incorporates cases within the exposure-mediator model to improve efficiency. Our approach does not require knowledge of disease prevalence and can model confounders and exposure-mediator interactions, and is straightforward to implement in standard statistical software. We illustrate our approach using both simulated data and real data from a case-control genetic study of lung cancer.

genetics↗

bioRxiv2021

Phasing Alleles Improves Network Inference with Allopolyploids

Accurately reconstructing the reticulate histories of polyploids remains a central challenge for understanding plant evolution. Although phylogenetic networks can provide insights into relationships among polyploid lineages, inferring networks may be hindered by the complexities of homology determination in polyploid taxa. We use simulations to show that phasing alleles from allopolyploid individuals can improve phylogenetic network inference under the multispecies coalescent by obtaining the true network with fewer loci compared to haplotype consensus sequences or sequences with heterozygous bases represented as ambiguity codes. Phased allelic data can also improve divergence time estimates for networks, which is helpful for evaluating allopolyploid speciation hypotheses and proposing mechanisms of speciation. To achieve these outcomes in empirical data, we present a novel pipeline that leverages a recently developed phasing algorithm to reliably phase alleles from polyploids. This pipeline is especially appropriate for target enrichment data, where depth of coverage is typically high enough to phase entire loci. We provide an empirical example in the North American Dryopteris fern complex that demonstrates insights from phased data as well as the challenges of network inference. We establish that our pipeline (PATE: Phased Alleles from Target Enrichment data) is capable of recovering a high proportion of phased loci from both diploids and polyploids. These data may improve network estimates compared to using haplotype consensus assemblies by accurately inferring the direction of gene flow, but statistical non-identifiability of phylogenetic networks poses a barrier to inferring the evolutionary history of reticulate complexes.

evolutionary biology↗