Biology subjects

Banos, H.

Publications and source records attributed to Banos, H..

13 recordsLinked to original sources

bioRxiv2026

Consensus methods for trees of blobs and level-1 phylogenetic networks

Phylogenetic networks are increasingly used to represent evolutionary histories involving hybridization, introgression, and other reticulate processes that cannot be captured by a single tree. Recent advances have produced numerous methods for inferring networks from genomic data. These methods may produce multiple plausible networks due to optimality ties, bootstrap analyses, Bayesian posterior samples, or differences in tools, datasets, and parameters. While consensus methods are well established for trees and some approaches exist for rooted networks, no consensus framework currently exists for semi-directed (unrooted) networks. We address this gap by introducing a new consensus framework for phylogenetic networks, rooted or semi-directed. We first develop a method for constructing a consensus tree of blobs, taking as input networks of any complexity. Building on this approach, we construct a consensus for level-1 networks (i.e., networks with non-overlapping cycles). This method, called 'L1N consensus', takes a collection of level-1 networks as input and returns a level-1 network. The different components of this consensus network (e.g., blobs, hybrid clades, and edges) have support values that correspond to the frequency of the given component among the input networks. These methods are implemented in PhyloSummaries, building on the foundation of PhyloNetworks. We evaluate the performance of L1N consensus using large-scale simulated data. Our results demonstrate that L1N consensus outperforms existing methods in the rooted setting in terms of informativity and accuracy. Similar results are obtained in the semi-directed setting. Applying L1N consensus to Leopardus and HIV datasets confirms these findings and demonstrates the usefulness of the method.

bioinformatics↗

bioRxiv2026

GTRspmix: Capturing Heterogeneity of Exchangeabilities Across Sites to Improve Protein Phylogenetics

Site rate and profile mixture models capture the heterogeneity of the amino acid substitution process across sites. However, these models typically use a single matrix of amino acid exchangeabilities and ignore potential heterogeneities of these exchangeabilities across sites. Simply combining multiple exchangeability matrices with rate and profile mixtures leads to a combinatorial explosion of mixture components and a prohibitive increase in free parameters. Here, we introduce GTRspmix, a novel framework that incorporates multiple exchangeability matrices into profile and site rate mixture models while effectively managing model complexity. GTRspmix employs a clustering-based strategy that groups profiles and assigns a distinct exchangeability matrix to each profile cluster. Evaluations using both empirical and simulated datasets demonstrate that GTRspmix fits empirical data significantly better than conventional models, and that overparameterization does not present a problem for sufficiently large alignments. Based on these results, we estimated general-purpose empirical models (SXXpfamCYY series available in IQ-TREE3) from the Pfam database. These general-purpose models not only fit data much better, but they also influence branch length and tree topology estimates, effectively mitigating long-branch attraction artifacts. Because the total number of rate matrices remains manageable, the computational efficiency of the inference is identical to that of conventional profile mixture models (e.g., LG+C60+G4). GTRspmix provides a more realistic and flexible model of protein evolution, offering a robust foundation for the inference of reliable phylogenetic trees.

evolutionary biology↗

bioRxiv2026

Statistical inference of the Tree of Blobs of a phylogenetic network from quartet concordance factors

A phylogenetic network represents evolutionary relationships involving hybridization, gene flow, or admixture. While the full network may not be identifiable from genomic data under common coalescent models, its tree of blobs, depicting only the tree-like portions of the network structure, is. We introduce ECToBlob (Edge Contraction for Tree of Blobs), a new statistically-consistent algorithm to estimate the tree of blobs from quartet concordance factors. Starting from a resolved tree, ECToBlob successively contracts edges which statistical tests indicate do not belong in the tree of blobs, due to reticulate or polytomous signal. We show that ASTRAL provides a valid starting tree under common assumptions, in that, asymptotically in the number of loci, trees optimizing ASTRALs criterion refine the tree of blobs. We describe several algorithm variants, differing in how evidence from multiple tests are combined to determine if the edge should be contracted, and provide software implementations. Relevance to Life SciencesHybridization, gene flow, or admixture are now recognized as important aspects of evolutionary history, but their genomic signal is confounded with that from a coalescent process, creating substantial challenges for inferring phylogenetic networks. The networks tree of blobs identifies areas where reticulation occurred, separated by tree-like branching. ECToBlob quickly estimates the tree of blobs using quartet concordance factors from gene trees, and provides a measure of statistical support for its result. Performance is illustrated through simulation and on empirical data, using an implementation in the R package MSCquartets. While the presence of a blob may be all that can be inferred in some cases, in others ECToBlob offers a robust and principled way to focus further analyses on more local reticulate structure. Mathematical ContentThis work makes contributions to mathematical phylogenetics in optimization, combinatorics, and statistics. We show that any tree maximizing quartet support (the criterion underlying ASTRAL) is a refinement of the networks tree of blobs under the coalescent model. Second, we give a concise proof that whether a network has a cut-edge corresponding to a given split is determined by information in certain subcollections of its 4-taxon subnetworks (quarnets). Finally, we propose valid statistical approaches for combining p-values across multiple quarnet hypothesis tests, proving that their use with specific decreasing test levels leads to statistically consistent inference as the number of loci grows. MSC codes05C90, 60J95, 62-04, 62F07, 92D15

evolutionary biology↗

bioRxiv2025

Nuclear-encoded tRNA genes harbor substantial allelic diversity in three nematode species

Cytosolic transfer RNAs, which are encoded as hundreds of genes in nuclear genomes, experience exceptionally high mutation rates and have been hypothesized to confer substantial mutational load in natural populations. Although this phenomenon appears universal across multicellular eukaryotes, a comprehensive characterization of standing variation in tRNA repertoires is still lacking in any system. Here, we resolve within-species allelic variation in nuclear-encoded tRNAs in three nematode species: Caenorhabditis elegans, C. briggsae, and C. tropicalis. We show that these genes carry signatures of high rates of historical transcription-associated mutagenesis and of purifying selection, resulting in allelic variation that includes pervasive instances of within-gene mismatches between the amino acid recognized by the tRNA backbone and that indicated by the anticodon. Furthermore, patterns of tRNA genomic organization and variation differ markedly from those of protein-coding regions. Individual genomes harbor distinct complements of tRNA genes with predicted functional differences, an observation that coincides with recent evidence that variation in tRNA expression and regulation contributes to human disease. Our findings offer an entry point for identifying the micro-evolutionary processes that act on tRNA repertoires, and in turn connecting those processes to the macro-evolutionary patterns that have more frequently been the focus of study. Significance statementTransfer RNAs (tRNAs) are ancient and essential molecules that deliver amino acids to the growing polypeptide chain during protein synthesis. Despite their importance, little is known about how the genes encoding tRNAs vary among individuals, even though they accumulate large numbers of mutations due to their high rates of expression. Here, we report that three nematode species, including the model organism C. elegans, show within-species variation in their tRNA gene repertoires, which may cause differences in protein synthesis across individuals. Our findings may represent patterns common across multicellular species and they set the stage for future investigation into how this diversity influences cellular function and organismal fitness.

genetics↗

bioRxiv2025

How much is Transcription-associated Mutagenesis Driving tRNA Microevolution?

Transfer RNAs (tRNAs) are among the most highly conserved and frequently transcribed genes. Recent studies have demonstrated that tRNAs experience exceptionally high rates of transcription-associated mutagenesis (TAM) as well as strong purifying selection. How the mutational input of TAM, which induces a non-uniform distribution of nucleotide substitutions, affects the fitness of tRNA molecules is unclear. Secondary structure in tRNAs is strongly conserved over macro-evolutionary time, suggesting that mutations that disrupt paired sites may be especially deleterious, but TAM-induced mutations primarily involve nucleotide transitions, which tend to preserve base-pairing stability. To examine how TAM affects tRNA molecule fitness and shapes tRNA evolution over short timescales, we analyzed tRNA allelic variation in contemporary Caenorhabditis elegans strains. We propose a model of tRNA microevolution driven by TAM and demonstrate that the observed secondary structure characteristics align with our predicted TAM-biased patterns. Furthermore, we developed a continuous Markov substitution model that incorporates TAM-specific mutational biases. This TAM-biased model fits the C. elegans tRNA data more effectively than standard models, such as the general time-reversible (GTR) model. Based on these results, we conclude that tRNAs in natural populations carry substantial levels of structure-destabilizing mutations, which may be tolerated but nevertheless likely induce meaningful fitness costs. Our findings are consistent with recent experimental studies on tRNA fitness in yeast but challenge prior theoretical and computational analyses that emphasize RNA base-pairing as a primary determinant in genotype-phenotype systems. 1 Significance StatementTransfer RNAs (tRNAs) are ancient molecules, encoded as genes in all living systems. tRNA genes are known to experience exceptionally high rates of both mutation and purifying selection, but how these opposing evolutionary forces shape tRNA evolution is unclear. We developed a sequence substitution model specific to tRNA mutagenesis and applied it to standing variation in a natural population in order to infer how mutation and selection affect the structural stability of tRNA molecules.

evolutionary biology↗

bioRxiv2024

NANUQ+: A divide-and-conquer approach to network estimation

Inference of a species network from genomic data remains a difficult problem, with recent progress mostly limited to the level-1 case. However, inference of the Tree of Blobs of a network, showing only the networks cut edges, can be performed for any network by TINNiK, suggesting a divide-and-conquer approach to network inference where the trees multifurcations are individually resolved to give more detailed structure. Here we develop a method, NANUQ+, to quickly perform such a level-1 resolution. Viewed as part of the NANUQ pipeline for fast level-1 inference, this gives tools for both understanding when the level-1 assumption is likely to be met and for exploring all highly-supported resolutions to cycles.

evolutionary biology↗

bioRxiv2024

A robustly rooted tree of eukaryotes reveals their excavate ancestry.

The eukaryote Tree of Life (eToL) depicts the relationships among all eukaryotic organisms; its root represents the last eukaryotic common ancestor (LECA) from which all extant complex lifeforms are descended. Locating this root is crucial for reconstructing the features of LECA, both as the endpoint of eukaryogenesis and the start-point for the evolution of the myriad complex traits underpinning the diversification of living eukaryotes. However, the position of the root remains contentious due to pervasive phylogenetic artefacts stemming from inadequate evolutionary models, poor taxon sampling, and limited phylogenetic signal. Here, we estimate the root of the eToL with unprecedented resolution based on a new, much larger, dataset of mitochondrial proteins which includes all known eukaryotic supergroups. Our comprehensive analyses with state-of-the-art phylogenetic models reveal that the eukaryotic root lies between two multi-supergroup assemblages: Opimoda+ and Diphoda+. Compellingly, this position is consistently supported across different models and robustness analyses. Notably, groups containing typical excavates are placed on both sides of the root, suggesting the complex features of the excavate cell architecture trace back to LECA. This study is the most comprehensive phylogenetic investigation of the eukaryote root to date, shedding light on the ancestral cells from which extant eukaryotes arose and providing a crucial framework for investigating the origin and evolution of canonical eukaryotic features.

evolutionary biology↗

bioRxiv2024

TINNiK: Inference of the Tree of Blobs of a Species Network Under the Coalescent

The tree of blobs of a species network shows only the tree-like aspects of relationships of taxa on a network, omitting information on network substructures where hybridization or other types of lateral transfer of genetic information occur. By isolating such regions of a network, inference of the tree of blobs can serve as a starting point for a more detailed investigation, or indicate the limit of what may be inferrable without additional assumptions. Building on our theoretical work on the identifiability of the tree of blobs from gene quartet distributions under the Network Multispecies Coalescent model, we develop an algorithm, TINNiK, for statistically consistent tree of blobs inference. We provide examples of its application to both simulated and empirical datasets, utilizing an implementation in the MSCquartets 2.0 R package. MSC Classification92D15, 92D20

evolutionary biology↗

bioRxiv2024

GTRpmix: A linked general-time reversible model for profile mixture models

Profile mixture models capture distinct biochemical constraints on the amino acid substitution process at different sites in proteins. These models feature a mixture of time-reversible models with a common set of amino acid exchange rates (a matrix of exchangeabilities) and distinct sets of equilibrium amino acid frequencies known as profiles. Combining the exchangeability matrix with each profile generates the matrix of instantaneous rates of amino acid exchange for that profile. Currently, empirically estimated exchangeability matrices (e.g., the LG or WAG matrices) are widely used for phylogenetic inference under profile mixture models. However, such matrices were originally estimated using site homogeneous models with a single set of equilibrium amino acid frequencies; therefore unlikely to be optimal for site heterogeneous profile mixture models. Here we describe the GTRpmix model, implemented in IQ-TREE2, that allows maximum likelihood estimation of a common set of exchangeabilities for all site classes under any profile mixture model. We show that exchangeability matrices estimated in the presence of a site-heterogeneous profile mixture model differ markedly from the widely used LG matrix and dramatically improve model fit and topological estimation accuracy for empirical test cases. Because the GTRpmix model is computationally expensive, we provide two exchangeability matrices estimated from large concatenated phylogenomic supermatrices under the C60 profile mixture model that can be used as fixed matrices for phylogenetic analyses. One of these, called Eukaryotic Linked Mixture (ELM), is designed for phylogenetic analysis of proteins encoded by nuclear genomes of eukaryotes, and the other, Eukaryotic and Archeal Linked mixture (EAL), for reconstructing relationships between eukaryotes and Archaea. These matrices when combined with profile mixture models fit data much better and have improved topology estimation relative to the empirical LG matrix combined with the same underlying mixture models. Version v2.3.1 of IQ-TREE2 implementing these models is available at www.iqtree.org.

molecular biology↗

bioRxiv2024

Complex Hybridization in a Clade of Polytypic Salamanders (Plethodontidae: Desmognathus) Uncovered by Estimating Higher-Level Phylogenetic Networks

Hybridization between incipient lineages is a common feature of ecomorphological diversification. We examine these phenomena in the Pisgah clade of Desmognathus salamanders from the southern Appalachian Mountains of the eastern United States. The group contains four to seven species exhibiting two discrete phenotypes, aquatic "shovel-nosed" and semi-aquatic "black-bellied" forms. These ecomorphologies are ancient and have apparently been transmitted repeatedly between lineages through introgression. Geographically proximate populations of both phenotypes exhibit admixture, and at least two black-bellied lineages have been produced via reticulations between shovel-nosed parentals, suggesting complex transmission dynamics. However, computational constraints currently limit our ability to reconstruct network radiations from gene-tree data. Available methods are limited to level-1 networks wherein reticulations do not share edges, and higher-level networks may be non-identifiable in many cases. We present a heuristic approach to recover information from higher-level networks across a range of potentially identifiable empirical scenarios, supported by theory and simulation. When extrinsic information indicating the location and direction of hybridization events is available, our method can yield successful estimates of non-level-1 networks, or at least a reduced possible set thereof. Phylogenomic data strongly support a single backbone topology with up to five overlapping hybrid edges. These results suggest an unusual mechanism of ecomorphological hybrid speciation, wherein a binary threshold trait causes hybrids to shift between two microhabitat niches, promoting ecological divergence between sympatric hybrids and parentals. This contrasts with other well-known systems in which hybrids exhibit intermediate, novel, or transgressive phenotypes. Finally, the genetic basis of these phenotypes is unclear and further data are needed to clarify the evolutionary basis of morphological changes with ecological consequences.

evolutionary biology↗

bioRxiv2023

Anomalous networks under the multispecies coalescent: theory and prevalence

AO_SCPLOWBSTRACTC_SCPLOWReticulations in a phylogenetic network represent processes such as gene flow, admixture, recombination and hybrid speciation. Extending definitions from the tree setting, an anomalous network is one in which some unrooted tree topology displayed in the network appears in gene trees with a lower frequency than a tree not displayed in the network. We investigate anomalous networks under the Network Multispecies Coalescent Model with possible correlated inheritance at reticulations. Focusing on subsets of 4 taxa, we describe a new algorithm to calculate quartet concordance factors on networks of any level, faster than previous algorithms because of its focus on 4 taxa. We then study topological properties required for a 4-taxon network to be anomalous, uncovering the key role of 32-cycles: cycles of 3 edges parent to a sister group of 2 taxa. Under the model of common inheritance, that is, when each gene tree coalesces within a species tree displayed in the network, we prove that 4-taxon networks are never anomalous. Under independent and various levels of correlated inheritance, we use simulations under realistic parameters to quantify the prevalence of anomalous 4-taxon networks, finding that truly anomalous networks are rare. At the same time, however, we find a significant fraction of networks close enough to the anomaly zone to appear anomalous, when considering the quartet concordance factors observed from a few hundred genes. These apparent anomalies may challenge network inference methods.

evolutionary biology↗

bioRxiv2022

Are profile mixture models over-parameterized?

Biochemical constraints on the admissible amino acids at specific sites in proteins leads to heterogeneity of the amino acid substitution process over sites in alignments. It is well known that phylogenetic models of protein sequence evolution that do not account for site heterogeneity are prone to long-branch attraction (LBA) artifacts. Profile mixture models were developed to model heterogeneity of preferred amino acids at sites via a finite distribution of site classes each with a distinct set of equilibrium amino acid frequencies. However, it is unknown whether the large number of parameters in such models associated with the many amino acid frequency classes can adversely affect tree topology estimates because of over-parameterization. Here we demonstrate theoretically that for long sequences, over-parameterization does not create problems for estimation with profile mixture models. Under mild conditions, tree, amino acid frequencies and other model parameters converge to true values as sequence length increases, even when there are large numbers of components in the frequency profile distributions. Because large sample theory does not necessarily imply good behavior for shorter alignments we explore performance of these models with short alignments simulated with tree topologies that are prone to LBA artifacts. We find that over-parameterization is not a problem for complex profile mixture models even when there are many amino acid frequency classes. In fact, simple models with few site classes behave poorly. Interestingly, we also found that misspecification of the amino acid frequency classes does not lead to increased LBA artifacts as long as the estimated cumulative distribution function of the amino acid frequencies at sites adequately approximates the true one. In contrast, misspecification of the amino acid exchangeability rates can severely negatively affect parameter estimation. Finally, we explore the effects of including in the profile mixture model an additional F-class representing the overall frequencies of amino acids in the data set. Surprisingly, the F-class does not help parameter estimation significantly, and can decrease the probability of correct tree estimation, depending on the scenario, even though it tends to improve likelihood scores.

evolutionary biology↗

bioRxiv2020

MSCquartets 1.0: Quartet methods for species trees and networks under the multispecies coalescent model in R

MSCquartets is an R package for species tree hypothesis testing, inference of species trees, and inference of species networks under the Multispecies Coalescent model of incomplete lineage sorting. Input for these analyses are collections of metric or topological locus trees which are then summarized by the quartets displayed on them. Results of hypothesis tests at user-supplied levels are displayed in a simplex plot by color-coded points. The package includes the QDC and WQDC algorithms for topological and metric species tree inference, and the NANUQ algorithm for level-1 topological species network inference, all of which give statistically consistent estimators under the model.

bioinformatics↗