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Minh, B. Q.

Publications and source records attributed to Minh, B. Q..

At least 19 recordsLinked to original sources
bioRxiv2026

DGM Is Dead; Long Live DGM-Star! A Quantization-Theoretic Approach to Gamma Rate Discretization

Modeling substitution rate heterogeneity as a discretized Gamma distribution is standard in phylogenetic inference. Ferretti et al. (2026) have recently demonstrated that the most commonly used equiprobable Gamma rate discretization, known simply as the discrete Gamma model (DGM), substantially overestimates average branch lengths in reconstructed phylogenies when the true rates are drawn from a continuous Gamma distribution. We propose an alternative method to discretize the Gamma rate distribution that considerably reduces the bias in estimated branch lengths and can be used as a drop-in replacement for the standard DGM. The method works especially well on large trees with short branches, yielding up to a 20-fold reduction in bias on simulated phylogenies. It is developed by applying quantization-theoretic tools to the analysis of a simplified star-phylogeny abstraction introduced by Ferretti et al. (2026), where the new method is unbiased by construction. We explain Ferretti et al.'s counterintuitive observation that increasing the number of rate categories can exacerbate the bias of the standard DGM, even though this should in principle make DGM a better approximation to the continuous case, and offer our method as an immediately available alternative.

evolutionary biology↗
bioRxiv2026

PartitionFinder-mAIC: Phylogenetic Partitioning using Marginal Akaike Information Criterion

Partition models are widely used in phylogenomic analyses to account for heterogeneous evolutionary processes across different regions or loci of a sequence alignment. How alignment regions are grouped into partitions (the partitioning scheme) affects both the degree of over- or under-parameterization and the accuracy of downstream phylogenetic inferences. PartitionFinder is a widely adopted framework for selecting an optimal partitioning scheme. Using Akaike information criterion (AIC) and Bayesian information criterion (BIC), PartitionFinder merges partitions with similar evolutionary processes to avoid model overfitting. However, AIC and BIC are based on conditional likelihoods that treat partition assignments as fixed, whereas the recently introduced marginal AIC (mAIC) averages site likelihoods over the models of all partitions, providing a more appropriate criterion for inferring global parameters such as tree topology and branch lengths (Susko et al. 2026). Here, we implement mAIC for partition models in IQ-TREE 3 and integrate it into the PartitionFinder algorithms. Using a range of simulated and empirical DNA and protein datasets, we show that PartitionFinder-mAIC yields partitioning schemes with fewer partitions than those selected by AIC or BIC, and additionally improves phylogenetic inference at most key branches of the green plant evolution. The new PartitionFinder-mAIC is available in IQ-TREE version 3.1.4 with the command-line option -merit mAIC.

evolutionary biology↗
bioRxiv2026

ProtFinder: An efficient machine learning framework for protein model selection on real data

Model selection is a fundamental step in phylogenetic analysis that determines the best-fit model of sequence evolution for a given multiple sequence alignment. Popular model selection methods, such as ModelFinder, rely on statistical information criteria, such as the Bayesian Information Criterion (BIC) or the Akaike Information Criterion (AIC). However, these approaches are computationally expensive and the use of information criteria has been the subject of ongoing discussion. Recently, machine learning has emerged as a promising approach for phylogenetic model selection in both nucleotide and protein sequence analyses. ModelDetector is currently the only machine learning-based method for amino acid substitution model selection. However, because ModelDetector was trained on simulated data, it does not perform well on real datasets. Another limitation is that it does not support different rate heterogeneity across sites (RHAS) models. To overcome these limitations, we introduce ProtFinder, an efficient machine learning framework for protein model selection that predicts amino acid substitution models, RHAS models, and amino acid frequency models. To enable ProtFinder to work with real datasets, we employed a transfer learning strategy consisting of three stages: (1) initial training on large-scale simulated data, (2) joint training on both simulated and real data, and (3) final fine-tuning using real data only. Experimental results show that ProtFinder outperformed ModelDetector in amino acid substitution model selection. ProtFinder achieved comparable accuracy to the maximum likelihood method ModelFinder for substitution model selection on medium and large MSAs. It performs slightly better than ModelFinder in RHAS model selection and substantially outperforms it in amino acid frequency model determination. Notably, ProtFinder is up to 1,400 times faster than ModelFinder in terms of inference time, making it particularly suitable for medium and large datasets.

evolutionary biology↗
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

CMAPLE 2: Fast and Accurate Phylogenetic Inference for Millions of Pathogen Genomes

Phylogenetic analysis is essential to genomic epidemiology, for example in tracing the origin and evolution of SARS-CoV-2 variants during the COVID-19 pandemic. We previously introduced CMAPLE, a single-threaded implementation of the MAPLE algorithm designed for large-scale epidemiological genomic datasets. CMAPLE can reconstruct phylogenetic trees from up to one million SARS-CoV-2 genomes. Here, we present CMAPLE 2, a multi-threaded version of CMAPLE with parallel sample placement and subtree pruning and regrafting (SPR) search algorithms. CMAPLE 2 also reduces memory consumption by compressing data structures using multiple references along the tree instead of a single reference genome. It further implements two advanced models of highly site- and nucleotide-specific mutation patterns as observed in pandemic-scale genome data. Additionally, CMAPLE 2 parallelizes SPR-based Tree Assessment (SPRTA), an efficient and interpretable approach for assessing phylogenetic tree uncertainty, and supports ancestral state and mutation inference via mutation-annotated tree (MAT) reconstruction. When inferring a phylogeny from 500,000 SARS-CoV-2 genomes using 48 CPU cores, CMAPLE 2 reduces runtime from 5 days (with sequential CMAPLE) to 9 hours (a 13-fold speedup) while decreasing peak RAM usage from 11.1 GB to 7.3 GB. CMAPLE 2 can now reconstruct a tree of nearly four million SARS-CoV-2 genomes from scratch within 12 days using 41 GB of RAM, a task that the sequential CMAPLE and MAPLE cannot realistically complete. CMAPLE 2 is applicable to many pathogen genome datasets and enhances our preparedness for future pandemics.

evolutionary biology↗
bioRxiv2026

Tryptophan became part of the universal genetic code post-LUCA

We evaluate whether tryptophan (W), widely thought to be the last of the 20 canonical amino acids added to the genetic code, was already present in the Last Universal Common Ancestor (LUCA). We reconstruct the evolutionary history of tryptophanyl-tRNA synthetase (WRS), the enzyme that attaches W to its tRNA, and the related tyrosyl-tRNA synthetase (YRS). We identify and exclude sequences derived from ancient recombination between archaeal and bacterial YRSs. Diverse rooting methods, including a novel approach exploiting time non-reversible evolution, all place the root between bacterial and archaeal YRS rather than between YRS and WRS. This supports post-LUCA WRS origination in Archaea, followed by its horizontal transfer to Bacteria. However, ancestral sequence reconstruction suggests that Archaea were depleted for W while Bacteria were not, and enzymes essential for W biosynthesis emerged in Bacteria. This suggests that W usage originated in Bacteria, with later WRS emergence in Archaea allowing the archaeal genetic code to converge with the bacterial code. The universality of the genetic code is usually attributed to common descent from LUCA, but the final step making the code universal was instead achieved by horizontal gene transfer. This gives credence to similar mechanisms for earlier steps in genetic code evolution.

evolutionary biology↗
bioRxiv2026

Modeling Site-Specific Mutation Patterns in Pandemic-Scale Phylogenetics

Models of genome evolution often account for different evolutionary rates at different genome positions due to, e.g., varying selective pressures or mutation rates. Recent evidence from millions of publicly shared SARS-CoV-2 genomes has revealed a more complex mutational landscape than can be modeled with existing approaches. Here, mutation rates are in fact not only highly position-specific, as currently modeled, but also nucleotide-specific; for example, specific mutations can occur very often at certain determined genome positions, while at the same positions other mutations might not be highly recurrent. Here, we propose and investigate a general model of genome evolution where each genome position is allowed to evolve under an independent, non-normalized substitution rate matrix describing site-specific rates of all mutation types ("Site-Specific Matrix" model, or SSM). We implement SSM in the efficient pandemic-scale phylogenetic inference software CMAPLE. Large-scale genomic epidemiological simulations suggest that, given enough data, SSM can accurately infer position- and nucleotide-specific substitution rates for more frequently observed nucleotides (typically the reference nucleotide), while other rates require higher levels of divergence. Simulations also show that SSM has a modest impact on the accuracy of phylogenetic tree estimation. We use SSM to analyze the evolution of millions of SARS-CoV-2 genomes and observe substantial mismatches between the substitution rates of classical rate variation models and our SSM estimates. These results suggest that classical models of rate variation are inadequate for modeling site-specific mutation patterns and that SSM is a useful alternative for large-scale genome analyses.

evolutionary biology↗
bioRxiv2026

A New Information Theoretic Approach Shows that Mixture Models Outperform Partitioned Models for Phylogenetic Analyses of Amino Acid Data

Partitioned and mixture models are widely employed in Maximum Likelihood phylogenetic analyses of large genomic datasets. Comparing the fit of the two types of models has been challenging, because standard information-theoretic approaches cannot be applied. Mixture models are increasingly popular for the analysis of amino acid datasets and can lead to different conclusions compared to partitioned models. This raises an important question - which type of model tends to perform better? Susko et al. (2026) recently introduced the marginal Akaike information criterion (mAIC), which allows mixture models and partitioned models to be directly compared for the first time. Here, we use the mAIC and a range of other approaches to compare the fit of mixture and partitioned models across a diverse set of empirical datasets. We show that mixture models are universally favoured on amino acid datasets. This has important implications for interpreting empirical analyses and suggests that continued development of mixture models is an important avenue for future research.

evolutionary biology↗
bioRxiv2025

piqtree: A Python Package for Seamless Phylogenetic Inference with IQ-TREE

piqtree (pronounced pie-cue-tree) is an easy to use, open-source Python package that provides Python script based control of IQ-TREEs phylogenetic inference engine. piqtree builds IQ-TREE as a Python package, presenting a library of Python functions for performing many of IQ-TREEs capabilities including phylogenetic reconstruction, ultrafast bootstrapping, branch length optimization, model selection, rapid neighbor-joining, alignment simulation, and more. As piqtree explicitly uses IQ-TREEs phylogenetic algorithms, the computational and statistical performance of piqtree equal that of IQ-TREE. Modestly higher memory usage may be expected owing to the Python runtime and the need to load the alignment in Python. By exposing IQ-TREEs algorithms within Python, piqtree offers users a greatly simplified experience in development of phylogenetic workflows through seamless interoperability with other Python libraries and tools mediated by the cogent3 package. It enables users to perform interactive phylogenetic analyses and visualization using, for instance, Jupyter notebooks. We present the key features available in the piqtree library and a small case study that showcases its interoperability. piqtree is distributed for use as a standard Python package at https://pypi.org/project/piqtree/, documentation is available at https://piqtree.readthedocs.io and source code at https://github.com/iqtree/piqtree.

bioinformatics↗
bioRxiv2025

Robust Phylogenetics

We present a robust phylogenetic inference method, called the trimmed log-likelihood method, which effectively identifies fast-evolving, saturated, or erroneous sites in both simulated and empirical multiple sequence alignments. This method avoids circularity by dynamically identifying and removing sites without relying on an initial tree, allowing the specific sites removed to change as tree topology and branch lengths are estimated. Our analyses demonstrate that this method outperforms existing approaches, such as the Slow-Fast method, Tree Independent Generation of Evolutionary Rate approach, and Le Quesne Probability statistics, by removing fewer sites while still inferring phylogenies with comparable or greater accuracy. Implemented in IQ-TREE2, the trimmed log-likelihood method is user-friendly with a simple command-line interface. However, challenges remain in addressing heterogeneous evolutionary processes including compositional biases, such as GC bias. Despite these challenges, our approach offers a practical solution for improving phylogenetic inference by automatically and dynamically identifying sites to down-weight during phylogenetic analyses. We recommended that researchers compare trees inferred by varying the proportion of down-weighted sites to monitor changes in tree topology and to identify a set of candidate tree topologies for further consideration.

evolutionary biology↗
bioRxiv2024

TreeFormer: A transformer-based tree rearrangement operation for phylogenetic reconstruction

Phylogenetic inference is a fundamental problem in biology, which studies the origins and evolutionary relationships among species. Popular phylogenetic inference methods, such as IQ-TREE, RAxML, and PHYML, typically utilize heuristic tree search algorithms to seek a phylogenetic tree that maximizes the likelihood of the observed genetic data. However, tree search is time-consuming and often prone to local optima. To address these issues, we introduce TreeFormer, a new Transformer-based tree rearrangement operation for tree search. Experimental results show that TreeFormer achieves higher accuracy than FastTree 2 when reconstructing trees from real alignments with fewer than 1000 sites.

evolutionary biology↗
bioRxiv2024

This is SPRTA: assessing phylogenetic confidence at pandemic scales

Phylogenetics plays a central role in evolutionary biology and genomic epidemiology. Assessing phylogenetic confidence and reliability is therefore crucial and methods to do this, such as Felsensteins bootstrap, are among the most used in modern science. However, methods based on Felsensteins bootstrap suffer from excessive computational demand, and are unsuitable for large datasets. Furthermore, most of these methods emerge from a cladistic framework which makes their results hard to interpret in the context of genomic epidemiology. We propose SPRTA (" SPR-based Tree Assessment"), an efficient and interpretable approach to assess confidence in phylogenetic trees. SPRTA shifts the paradigm of phylogenetic support measurement from evaluating the confidence in clades (groupings of taxa) to genome evolution histories, for example assessing if a lineage evolved from another considered lineage or not. This focus on evolutionary histories is particularly valuable in genomic epidemiology, where typically the evolutionary and transmission history of a pathogen are of interest, not clade content. We illustrate the use of SPRTA by investigating a global SARS-CoV-2 phylogenetic tree relating > 2M genomes, highlighting plausible alternative evolutionary origins of many SARS-CoV-2 variants. We have implemented SPRTA within the free and open source maximum likelihood phylogenetic software MAPLE, available from https://github.com/NicolaDM/MAPLE.

bioinformatics↗
bioRxiv2024

Rate variation and recurrent sequence errors in pandemic-scale phylogenetics

Phylogenetic analyses of genome sequences from infectious pathogens reveal essential information regarding their evolution and transmission, as seen during the COVID-19 pandemic. Recently developed pandemic-scale phylogenetic inference methods reduce the computational demand of phylogenetic reconstruction from genomic epidemiological datasets, allowing the analysis of millions of closely related genomes. However, widespread homoplasies, due to recurrent mutations and sequence errors, cause phylogenetic uncertainty and biases. We present new algorithms and models to substantially improve the computational performance and accuracy of pandemic-scale phylogenetics. In particular, we account for, and identify, mutation rate variation and recurrent sequence errors. We reconstruct reliable and public sequence alignment and phylogenetic tree of > 2 million SARS-CoV-2 genomes encapsulating the evolutionary history and global spread of the virus up to February 2023.

bioinformatics↗
bioRxiv2024

CMAPLE: efficient phylogenetic inference in the pandemic era

We have recently introduced MAPLE (MAximum Parsimonious Likelihood Estimation), a new pandemic-scale phylogenetic inference method exclusively designed for genomic epidemiology. In response to the need for enhancing MAPLEs performance and scalability, here we present two key components: (1) CMAPLE software, a highly optimized C++ reimplementation of MAPLE with many new features and advancements; and (2) CMAPLE library, a suite of Application Programming Interfaces to facilitate the integration of the CMAPLE algorithm into existing phylogenetic inference packages. Notably, we have successfully integrated CMAPLE into the widely used IQ-TREE 2 software, enabling its rapid adoption in the scientific community. These advancements serve as a vital step towards better preparedness for future pandemics, offering researchers powerful tools for large-scale pathogen genomic analysis.

evolutionary biology↗
bioRxiv2024

Identifying the Last Universal Common Ancestor's protein domains resolves the order in which the amino acids were recruited into the genetic code

The current "consensus" order in which amino acids were added to the genetic code is based on potentially biased criteria, such as absence of sulfur-containing amino acids from the Urey-Miller experiment which lacked sulfur. More broadly, abiotic abundance might not reflect biotic abundance in the organisms in which the genetic code evolved. Here, we instead identify which protein domains date to the last universal common ancestor (LUCA), then infer the order of recruitment from deviations of their ancestrally reconstructed amino acid frequencies from the still-ancient post-LUCA controls. We find that smaller amino acids were added to the code earlier, with no additional predictive power in the previous "consensus" order. Metal-binding (cysteine and histidine) and sulfur-containing (cysteine and methionine) amino acids were added to the genetic code much earlier than previously thought. Methionine and histidine were added to the code earlier than expected from their molecular weights, and glutamine later. Early methionine availability is compatible with inferred early use of S-adenosylmethionine, and early histidine with its purine-like structure and the demand for metal-binding. Even more ancient protein sequences -- those that had already diversified into multiple distinct copies prior to LUCA -- have significantly higher frequencies of aromatic amino acids (tryptophan, tyrosine, phenylalanine and histidine), and lower frequencies of valine and glutamic acid than single copy LUCA sequences. If at least some of these sequences predate the current code, then their distinct enrichment patterns provide hints about earlier, alternative genetic codes. Significance StatementThe order in which the amino acids were added to the genetic code was previously inferred from consensus among forty metrics. Many of these reflect abiotic abundance on ancient Earth. However, the abundances that matter are those within primitive cells that already had sophisticated RNA and perhaps peptide metabolism. Here, we directly infer the order of recruitment from the relative ancestral amino acid frequencies of ancient protein sequences. Small size predicts ancient amino acid enrichment better than the previous consensus metric does. We place metal-binding and sulfur-containing amino acids earlier than previously thought, highlighting the importance of metal-dependent catalysis and sulfur metabolism to ancient life. Understanding early life has implications for our search for life elsewhere in the universe.

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

MixtureFinder: Estimating DNA mixture models for phylogenetic analyses

In phylogenetic studies, both partitioned models and mixture models are used to account for heterogeneity in molecular evolution among the sites of DNA sequence alignments. Partitioned models require the user to specify the grouping of sites into subsets, and then assume that each subset of sites can be modelled by a single common process. Mixture models do not require users to pre-specify subsets of sites, and instead calculate the likelihood of every site under every model, while co-estimating the model weights and parameters. While much research has gone into the optimisation of partitioned models by merging user-specified subsets, there has been less attention paid to the optimisation of mixture models for DNA sequence alignments. In this study, we first ask whether a key assumption of partitioned models - that each user-specified subset can be modelled by a single common process - is supported by the data. Having shown that this is not the case, we then design, implement, test, and apply an algorithm, MixtureFinder, to select the optimum number of classes for a mixture model of Q matrices for the standard models of DNA sequence evolution. We show this algorithm performs well on simulated and empirical datasets and suggest that it may be useful for future empirical studies. MixtureFinder is available in IQ-TREE2, and a tutorial for using MixtureFinder can be found here: http://www.iqtree.org/doc/Complex-Models#mixture-models.

evolutionary biology↗
bioRxiv2023

AliSim-HPC: parallel sequence simulator for phylogenetics

MotivationSequence simulation plays a vital role in phylogenetics with many applications, such as evaluating phylogenetic methods, testing hypotheses, and generating training data for machine-learning applications. We recently introduced a new simulator for multiple sequence alignments called AliSim, which outperformed existing tools. However, with the increasing demands of simulating large data sets, AliSim is still slow due to its sequential implementation; for example, to simulate millions of sequence alignments, AliSim took several days or weeks. Parallelization has been used for many phylogenetic inference methods but not yet for sequence simulation. ResultsThis paper introduces AliSim-HPC, which, for the first time, employs high-performance computing for phylogenetic simulations. AliSim-HPC parallelizes the simulation process at both multi-core and multi-CPU levels using the OpenMP and MPI libraries, respectively. AliSim-HPC is highly efficient and scalable, which reduces the runtime to simulate 100 large alignments from one day to 9 minutes using 256 CPU cores from a cluster with 6 computing nodes, a 162-fold speedup. Availability and implementationAliSim-HPC is open source and available as part of the new IQ-TREE version v2.2.2.2 at https://github.com/iqtree/iqtree2/releases with a user manual at http://www.iqtree.org/doc/AliSim. Contactm.bui@anu.edu.au

evolutionary biology↗