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At least 19 recordsLinked to original sources
PubMed2026

Bit-reproducible parallel phylogenetic tree inference.

MOTIVATION: Phylogenetic trees describe the evolutionary history among biological species based on their genomic data. Maximum likelihood (ML) based phylogenetic inference tools search for the tree and evolutionary model that best explain the observed genomic data. Given the independence of likelihood score calculations between different genomic sites, parallel computation is commonly deployed. This is followed by a parallel summation over the per-site scores to obtain the overall likelihood score of the tree. However, basic arithmetic operations on IEEE 754 floating-point numbers, such as addition and multiplication, inherently introduce rounding errors. Consequently, the order by which floating-point operations are executed affects the exact resulting likelihood value since these operations are not associative. Moreover, parallel reduction algorithms in numerical codes re-associate operations as a function of the core count and cluster network topology, inducing different round-off errors. These low-level deviations can cause heuristic searches to diverge and induce high-level result discrepancies (e.g. yield topologically distinct phylogenies). This effect has also been observed in multiple scientific fields beyond phylogenetics. RESULTS: We observe that varying the degree of parallelism results in diverging phylogenetic tree searches (high-level results) for over 31% out of 10&#xa0;179 empirical datasets. More importantly, 8% of these diverging datasets yield trees that are statistically significantly worse than the best-known ML tree for the dataset (AU-test, P&#x2009;<&#x2009;.05). To alleviate this, we develop a variant of the widely used phylogenetic inference tool RAxML-NG, which does yield bit-reproducible results under varying core-counts, with a slowdown of only 0%-12.7% (median 0.8%) on up to 768 cores. For this, we introduce the ReproRed reduction algorithm, which yields bit-identical results under varying core-counts, by maintaining a fixed operation order that is independent of the communication pattern. ReproRed is thus applicable to all associative reduction operations-in contrast to competitors, which are confined to summation. Our ReproRed reduction algorithm only exchanges the theoretical minimum number of messages, overlaps communication with computation, and utilizes fast base-cases for local reductions. ReproRed is able to all-reduce (via a subsequent broadcast) 4.1&#xd7;106 operands across 48-768 cores in 19.7-48.61&#x2009;&#x3bc;s, thereby exhibiting a slowdown of 13%-93% over a non-reproducible all-reduce algorithm. ReproRed outperforms the state-of-the-art reproducible all-reduction algorithm ReproBLAS (offers summation only) beyond 10&#xa0;000 elements per core. In summary, we re-assess non-reproducibility in parallel phylogenetic inference, present the first bit-reproducible parallel phylogenetic inference tool, as well as introduce a general algorithm and open-source code for conducting reproducible associative parallel reduction operations. AVAILABILITY AND IMPLEMENTATION: ReproRed: https://doi.org/10.5281/zenodo.15004918 (LGPL)-Reproducible RAxML-NG version https://doi.org/10.5281/zenodo.15017407 (GPL).

Phylogeny↗
PubMed2026

Treemble: a graphical tool to generate Newick strings from phylogenetic tree images.

SUMMARY: Phylogenetic trees are ubiquitous and central to biology, but most published trees are available only as visual diagrams and not in the machine-readable Newick format. There are, thus, thousands of published trees in the scientific literature that are unavailable for follow-up analyses, comparisons, and supertree construction. Experts can easily read such diagrams, but the manual construction of a Newick string from a diagram is laborious, error-prone, and time-consuming. Previous attempts to semi-automate the reading of tree images relied on image processing techniques. These often encounter difficulties as typical published tree diagrams contain various graphical elements and annotations that overlap the branches, such as error bars on internal nodes. Here we introduce Treemble, a user-friendly desktop application for generating Newick strings from tree images. The user simply clicks to mark node locations, assisted by a deep learning-based node detection tool, and Treemble algorithmically assembles the tree from the node coordinates alone. Treemble also facilitates the automatic reading of tip name labels and can be used for both rectangular and circular trees. AVAILABILITY AND IMPLEMENTATION: Treemble is a native desktop application for macOS and Windows and is freely available, with documentation, at treemble.org. Source code is available at github.com/John-Allard/Treemble. The trained node detection model is available at huggingface.co/John-Allard/treemble-1.

Phylogeny↗
PubMed1979

A graph theoretic approach to the development of minimal phylogenetic trees.

The problem of determining the minimal phylogenetic tree is discussed in relation to graph theory. It is shown that this problem is an example of the Steiner problem in graphs which is to connect a set of points by a minimal length network where new points can be added. There is no reported method of solving realistically-sized Steiner problems in reasonable computing time. A heuristic method of approaching the phylogenetic problem is presented, together with a worked example with 7 mammalian cytochrome c sequences. It is shown in this case that the method develops a phylogenetic tree that has the smallest possible number of amino acid replacements. The potential and limitations of the method are discussed. It is stressed that objective methods must be used for comparing different trees. In particular it should be determined how close a given tree is to a mathematically determined lower bound. A theorem is proved which is used to establish a lower bound on the lenghtof any tree and if a tree is found with a length equal to the lower bound, then no shorter tree can exist.

Mathematics↗
PubMed1979

II. On the construction of a phylogenetic tree.

For the construction of a phylogenetic tree in algorithm is described. This, allows the correction of the original data and the proper selection, in each step of the process, of the nearest neighbours of a common ancestor.

Animals↗
PubMed2023

Inference of phylogenetic trees directly from raw sequencing reads using Read2Tree.

Current methods for inference of phylogenetic trees require running complex pipelines at substantial computational and labor costs, with additional constraints in sequencing coverage, assembly and annotation quality, especially for large datasets. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes and bypasses traditional steps in phylogeny inference, such as genome assembly, annotation and all-versus-all sequence comparisons, while retaining accuracy. In a benchmark encompassing a broad variety of datasets, Read2Tree is 10-100 times faster than assembly-based approaches and in most cases more accurate-the exception being when sequencing coverage is high and reference species very distant. Here, to illustrate the broad applicability of the tool, we reconstruct a yeast tree of life of 435 species spanning 590 million years of evolution. We also apply Read2Tree to >10,000 Coronaviridae samples, accurately classifying highly diverse animal samples and near-identical severe acute respiratory syndrome coronavirus 2 sequences on a single tree. The speed, accuracy and versatility of Read2Tree enable comparative genomics at scale.

Animals↗
PubMed1979

A general approach to proving the minimality of phylogenetic trees illustrated by an example with a set of 23 vertebrates.

We have recently described a method of building phylogenetic trees and have outlined an approach for proving whether a particular tree is optimal for the data used. In this paper we describe in detail the method of establishing lower bounds on the length of a minimal tree by partitioning the data set into subsets. All characters that could be involved in duplications in the data are paired with all other such characters. A matching algorithm is then used to obtain the pairing of characters that reveals the most duplications in the data. This matching may still not account for all nucleotide substitutions on the tree. The structure of the tree is then used to help select subsets of three or more characters until the lower bound found by partitioning is equal to the length of the tree. The tree must then be a minimal tree since no tree can exist with a length less than that of the lower bound. The method is demonstrated using a set of 23 vertebrate cytochrome c sequences with the criterion of minimizing the total number of nucleotide substitutions. There are 131130 7045768798 96033440625 topologically distinct trees that can be constructed from this data set. The method described in this paper does identify 144 minimal tree variants. The method is general in the sense that it can be used for other data and other criteria of length. It need not however always be possible to prove a treee minimal but the method will give an upper and lower bound on the length of minimal trees.

Amino Acid Sequence↗
PubMed1978

Construction of phylogenetic trees for proteins and nucleic acids: empirical evaluation of alternative matrix methods.

The methods of Fitch and Margoliash and of Farris for the construction of phylogenetic trees were compared. A phenetic clustering technique--the UPGMA method--was also considered. The three methods were applied to difference matrices obtained from comparison of macromolecules by immunological, DNA hybridization, electrophoretic, and amino acid sequencing techniques. To evaluate the results, we used the goodness-of-fit criterion. In some instances, the F-M and Farris methods gave a comparably good fit of the output to the input data, though in most cases the F-M procedure gave a much better fit. By the fit criterion, the UPGMA procedure was on the average better than the Farris method but not as good as the F-M procedure. On the basis of the results given in this report and the goodness-of-fit criterion, it is suggested that where input data are likely to include overestimates as well as true estimates and underestimates of the actual distances between taxonomic units, the F-M method is the most reasonable to use for constructing phylogenies from distance matrices. Immunological, DNA hybridization, and electrophoretic data fall into this category. By contrast, where it is known that each input datum is indeed either a true estimate or an underestimate of the actual distance between 2 taxonomic units, the Farris procedure appears, on theoretical grounds, to be the matrix method of choice. Amino acid and nucleotide sequence data are in this category.

Animals↗
PubMed2026

treestructure: an R package to detect population structure in phylogenetic trees.

MOTIVATION: How population structure can shape genetic diversity is a longstanding problem in population genetics. While the use of geographic locations, when available, can help answer some of these questions, it is still difficult to determine population structure when such metadata are not available or when the potential population structure is not easily observed. Here, we present an updated version of treestructure, an R package that implements a statistical test based on coalescent theory to detect unobserved population structure in a time-scaled phylogenetic tree. AVAILABILITY: treestructure is available at CRAN at https://cloud.r-project.org/web/packages/treestructure/ and at https://emvolz-phylodynamics.github.io/treestructure/.

Phylogeny↗
PubMed2026

TreeFlow: Probabilistic Modelling and Automatic Differentiation for Phylogenetics.

Probabilistic modelling frameworks are powerful tools for statistical modelling and inference. They are not immediately generalizable to phylogenetic problems due to the particular computational properties of the phylogenetic tree object. TreeFlow is a software library for probabilistic modelling and automatic differentiation with phylogenetic trees. It embeds phylogenetic trees in the TensorFlow Probability framework, and implements inference algorithms for phylogenetic models given a fixed tree topology. We demonstrate how TreeFlow can be used to quickly implement and assess new models. We also show that it provides reasonable performance for gradient-based inference algorithms compared to specialized computational libraries for phylogenetics.

Bayesian inference↗
PubMed2025

A comprehensive phylogeny of mammalian PRNP gene reveals no influence of prion misfolding propensity on the evolution of this gene.

Prion diseases are invariably fatal neurodegenerative diseases that affect some mammalian species, including humans. These diseases are caused by the misfolding of the cellular prion protein (PrPC) into a pathologic isoform (PrPSc). The prion protein is highly conserved across mammals. However, some species present lower susceptibility to prion diseases than others. This behavior is likely explained by the resistance of these animal species' prion proteins to acquire a pathological conformation. Therefore, the tertiary structure and interspecific variations encoded in the primary structure determine a PrP proneness to misfolding. For this reason, we studied the PRNP gene from a phylogenetic perspective, potentially unveiling evolutionary events related to prion diseases. We generated a database of mammalian PRNP sequences and constructed phylogenetic trees based on nucleotide sequence variations. We aligned 1146 PRNP gene sequences from 901 different mammalian species and built a PRNP gene-based phylogenetic tree. Classical phylogenetic orders tend to maintain their clustering in the PRNP gene tree. Nonetheless, the few differences found may shed some light on potential evolutionary constraints posed by prion disorders. Moreover, this phylogenetic study was combined with an in vitro misfolding study. Protein Misfolding Shaking Amplification (PMSA) was used to evaluate the tendency of many of these proteins to misfold. This comprehensive analysis spanned a wide range of mammalian prion protein sequences and included analysis of different variants with a focus on the human rs1799990 locus (c.385A&#x2009;>&#x2009;G, p.Met129Val). This variant, widely linked to prion disease susceptibility in humans, is explored in the context of its evolutionary origins. All in all, our PRNP gene-based tree, despite showing some topological differences with the reference species tree that could be in some cases related to prion disease susceptibility, is not significantly distinct. Indicating that the proneness of a PrP variant to misfold spontaneously has not shaped the evolution of this gene.

Phylogeny↗
PubMed2025

Accounting for contact tracing in epidemiological birth-death models.

Phylodynamics bridges the gap between classical epidemiology and pathogen genome sequence data by estimating epidemiological parameters from time-scaled pathogen phylogenetic trees. The models used in phylodynamics typically assume that the sampling procedure is independent between infected individuals. However, this assumption does not hold for many epidemics, in particular for such sexually transmitted infections as HIV-1, for which contact tracing schemes are included in health policies of many countries. We extended phylodynamic multi-type birth-death (MTBD) models with contact tracing (CT), and developed a simulator to generate trees under MTBD and MTBD-CT models. We proposed a non-parametric test for detecting contact tracing in pathogen phylogenetic trees. Its application to simulated data showed that it is both highly specific and sensitive. For the simplest representative of the MTBD-CT family, the BD-CT(1) model, where only the last contact can be notified, we solved the differential equations and proposed a closed form solution for the likelihood function. We implemented a maximum-likelihood program, which estimates the BD-CT(1) model parameters and their confidence intervals from phylogenetic trees. It performed accurate parameter inference on BD and BD-CT(1) simulated data, and detected contact tracing in HIV-1 B epidemics in Zurich and the UK. Importantly, we showed that not accounting for contact tracing when it is present, leads to bias in parameter estimation with the BD model (overestimation of the becoming-non-infectious rate). This bias is also present, but greatly reduced, when the BD-CT(1) model is used on data where multiple contacts can be notified. Our CT test, MTBD-CT tree simulator and BD-CT(1) parameter estimator are freely available at GitHub (evolbioinfo/treesimulator and evolbioinfo/bdct).

Contact Tracing↗
PubMed2024

A framework for automated scalable designation of viral pathogen lineages from genomic data.

Pathogen lineage nomenclature systems are a key component of effective communication and collaboration for researchers and public health workers. Since February 2021, the Pango dynamic lineage nomenclature for SARS-CoV-2 has been sustained by crowdsourced lineage proposals as new isolates were sequenced. This approach is vulnerable to time-critical delays as well as regional and personal bias. Here we developed a simple heuristic approach for dividing phylogenetic trees into lineages, including the prioritization of key mutations or genes. Our implementation is efficient on extremely large phylogenetic trees consisting of millions of sequences and produces similar results to existing manually curated lineage designations when applied to SARS-CoV-2 and other viruses including chikungunya virus, Venezuelan equine encephalitis virus complex and Zika virus. This method offers a simple, automated and consistent approach to pathogen nomenclature that can assist researchers in developing and maintaining phylogeny-based classifications in the face of ever-increasing genomic datasets.

Animals↗
PubMed2026

A New Species of Eucnemidae (Coleoptera: Elateroidea) with Its Complete Mitogenome and Mitogenome-Based Phylogenetic Analysis.

We describe Microrhagus ziwulingensis Muona & Meng, sp. nov., from China. The genus Microrhagus Dejean, 1833, was previously represented in China by only two species. We sequenced and assembled the complete mitogenome of M. ziwulingensis (GenBank accession OK143440), which encoded 13 protein-coding genes (PCGs), 2 ribosomal RNA genes (rRNAs), 22 transfer RNA genes (tRNAs), and a putative control region with a total length of 15,843 bp. Overall, 36 species of Elateroidea were collected as the ingroup (for six of these species, two sequences of the same species submitted by different submitters were used). Eight species of Buprestoidea served as the outgroup. We constructed phylogenetic trees using maximum likelihood (ML) and Bayesian inference (BI) methods based on 13 protein-coding genes (PCGs) from mitochondrial genomes. The phylogenetic trees showed that all families within the superfamily Elateroidea, which was used as the ingroup, formed monophyletic groups. The topology differed from previous studies, showing that Rhagophthalmidae and Lampyridae formed a sister clade, and that Phengodidae + Lycidae, with Cantharidae, formed a sister clade. These discrepancies should be attributed to the use of a single type of molecular marker and the intense shortage of available sampling. This also indicates that mitochondrial genomic research on Eucnemidae, and even on the superfamily Elateroidea, still needs further expansion.

Microrhagus↗
PubMed2026

GraphyloVar: predicting the impact of non-coding variants using a multi-species sequence model.

MOTIVATION: Understanding the functional impact of genetic variants is a key problem for precision medicine. Tools like CADD, PhyloP, and PhastCons are useful, but they often look at each position in the genome in isolation. This means they can miss important information from the evolutionary history that connects different species. In this paper, we extend our previous model, Graphylo, to predict the effects of variants. Our new model, GraphyloVar, is built to directly utilize the phylogenetic tree that relates the species. RESULTS: GraphyloVar is a deep learning model that considers both DNA sequence and evolutionary patterns from many species. It uses two main components: Graph Convolutional Networks (GCNs) to process the phylogenetic tree, and Transformer encoders to extract features from the DNA sequences. Pre-trained to predict population-level allele frequencies on the TOPMed whole-genome sequencing cohort, GraphyloVar achieves an AUROC of 0.6246 zero-shot on &#x223c;149M held-out variants, and an ensemble with CADD reaches 0.6442 (+0.020, P<10-15). Fine-tuned GraphyloVar achieves the highest AUROC across all 13 MPRA benchmark datasets. By integrating deep learning with explicit phylogenetic input, GraphyloVar offers a powerful and complementary approach to variant effect prediction that utilizes the full evolutionary history from many species to better identify and prioritize important non-coding variants. AVAILABILITY AND IMPLEMENTATION: Code and datasets are available at https://github.com/DongjoonLim/GraphyloVar under DOI: 10.5281/zenodo.20616818.

Phylogeny↗
PubMed2022

Revisiting Papillomavirus Taxonomy: A Proposal for Updating the Current Classification in Line with Evolutionary Evidence.

Papillomaviruses infect a wide array of animal hosts and are responsible for roughly 5% of all human cancers. Comparative genomics between different virus types belonging to specific taxonomic groupings (e.g., species, and genera) has the potential to illuminate physiological differences between viruses with different biological outcomes. Likewise, extrapolation of features between related viruses can be very powerful but requires a solid foundation supporting the evolutionary relationships between viruses. The current papillomavirus classification system is based on pairwise sequence identity. However, with the advent of metagenomics as facilitated by high-throughput sequencing and molecular tools of enriching circular DNA molecules using rolling circle amplification, there has been a dramatic increase in the described diversity of this viral family. Not surprisingly, this resulted in a dramatic increase in absolute number of viral types (i.e., sequences sharing <90% L1 gene pairwise identity). Many of these novel viruses are the sole member of a novel species within a novel genus (i.e., singletons), highlighting that we have only scratched the surface of papillomavirus diversity. I will discuss how this increase in observed sequence diversity complicates papillomavirus classification. I will propose a potential solution to these issues by explicitly basing the species and genera classification on the evolutionary history of these viruses based on the core viral proteins (E1, E2, and L1) of papillomaviruses. This strategy means that it is possible that a virus identified as the closest neighbor based on the E1, E2, L1 phylogenetic tree, is not the closest neighbor based on L1 nucleotide identity. In this case, I propose that a virus would be considered a novel type if it shares less than 90% identity with its closest neighbors in the E1, E2, L1 phylogenetic tree.

Animals↗