Biology subjects

Stamatakis, A.

Publications and source records attributed to Stamatakis, A..

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

RAxML-NG 2: Automatic model selection, novel tree search heuristics, and fast branch support metrics

RAxML-NG is a widely used tool for maximum likelihood based phylogenetic inference. In the seven years since the last RAxML-NG publication, we have continuously improved and extended the code. Here, we describe the next major release, RAxML-NG 2.0. It introduces a plethora of new features: integrated model testing, multiple fast branch support metrics, automatic parallelization tuning, phylogenetic difficulty prediction, genotype evolution models, to name but the most important ones. Furthermore, we introduce two novel search heuristics at production code level: the adaptive difficulty-aware heuristic (default) and the fast mode with early-stopping that prevents over-optimization. We perform extensive benchmarking of RAxML-NG 2.0 with respect to its accuracy and speed, and compare it to other popular maximum likelihood based phylogenetic inference tools (IQTree, VeryFastTree) as well as to preceding RAxML-NG versions. In particular, the new fast search heuristic in conjunction with machine learning based branch support prediction induces a 75x inference time reduction compared to RAxML-NG 1.2, with minor to no accuracy loss. The code is available under GNU GPL at https://codeberg.org/amkozlov/raxml-ng.

bioinformatics↗

bioRxiv2026

A large-scale evaluation of tree shape indices reveals potential pitfalls

While there exists a plethora of prior research on phylogenetic tree shape indices, a large-scale analysis of the behavior of these indices on empirical trees has not yet been conducted.Here, we address this by computing 54 indices on more than 45,000,000 empirical trees retrieved from the EvoNAPS and RAxML Grove databases. To calculate the indices, we use our novel, comprehensive open-source Python library called treeshapy. The results of our large-scale evaluation indicate that there exist several potential pitfalls when conducting tree shape studies. For $14$ indices we find clear indications, that they are highly sensitive to the position of the tree's root. Only $5$ indices appear stable in that sense, while we observe a medium degree of rooting instability for the remaining $35$ indices under study. Therefore, uncertainties pertaining to the root placement directly affect tree shape values. Furthermore, the values of all except $5$ indices are inherently correlated with tree size, even so, when applying adequate normalization techniques. As a consequence, tree shape values for trees of different sizes should generally not be compared. We further observe that several groups of indices are strongly correlated with one another. Hence, to conduct a representative tree shape study, an appropriate subset of uncorrelated indices should be selected to capture as many aspects of the tree shape as possible while avoiding essentially redundant results at the same time. Our study serves as a guide for conducting tree shape studies in a more cautious and comprehensive manner on empirical data. Apart from our open-source treehshapy Python package, we devise appropriate guidelines for selecting suitable indices and cautiously interpreting respective results.

evolutionary biology↗

bioRxiv2026

Charting the insect biodiversity of Crete: insights from a pilot metabarcoding survey

Among eukaryotes, insects are by far the most diverse organisms on Earth, yet their global decline threatens ecosystem stability. Understanding local and regional biodiversity patterns is critical for conservation planning, ecosystem management, and predicting responses to environmental change, but traditional surveys for assessing insect diversity (e.g., manual collection, morphological identification, and counting) are highly labor-intensive, time-consuming, and often require rare or simply unavailable dedicated taxonomic expertise. DNA metabarcoding offers an efficient, high-resolution alternative to assess insect communities. Here, we report on the first insect metabarcoding survey on Crete that spans two years of sample collection between 2021 and 2023 from a small area in Southern Central Crete in the context of a citizen science project. A total of 29 samples yielded 10,865 Exact Sequence Variants (ESVs), 10,516 of which were assigned to insects, covering 988 species, 900 genera, and 227 families across 14 orders. A comparison with the existing observation records reveals 406 potential newly-observed species and an estimated 690 unclassified species, indicating substantial cryptic diversity. Our results demonstrate that even small-scale sampling can unravel substantial insect diversity and highlight critical gaps in barcode reference databases. Our study demonstrates how DNA metabarcoding can accelerate biodiversity discovery and monitoring in understudied regions.

ecology↗

bioRxiv2026

Quantifying and Predicting the Difficulty of Multiple Sequence Alignment with AlDiScore

Multiple Sequence Alignment (MSA) constitutes an important and frequent operation in molecular sequence data analysis. There exist numerous tools, algorithms, and criteria to infer an MSA. This plethora of available approaches to MSA may induced an ensemble of divergent MSAs for the same underlying unaligned sequence set. Even a single MSA tool may infer distinct MSAs when varying the input parameters. Hence, when using a diversified set of MSA algorithms and parameterizations, the observed dispersion within an MSA ensemble expresses the difficulty of inferring a robust alignment. We refer to this notion as MSA difficulty. As downstream analyses heavily rely on the MSA, characterizing MSA difficulty for a given unaligned sequence set is critical. Initially, we show that measures of dispersion within diversified MSA ensembles can reliably predict MSA difficulty. We then assess the adequacy of these measures by computing the average reference-based distance between the MSAs in the MSA ensemble and its corresponding structural reference MSA and subsequently comparing this distance to the corresponding reference-free average distance over all MSA pairs in the ensemble. We find that Blackburne and Whelans dpos alignment metric is most appropriate as its reference-free [Formula] counterpart most accurately approximates the reference-based difficulty computed on BAliBASE reference data. We therefore use [Formula] to quantify MSA difficulty on a scale from 0 (easy) to 1 (difficult). Next, we introduce the AlDiScore open-source tool, which uses machine learning to directly and reliably predict reference-free difficulty scores from unaligned sequence sets to completely omit expensive MSA computations. The underlying regression model relies upon a large set of features, including sampling-based measures of transitive consistency. We trained our AlDiScore model on a diverse collection of empirical datasets from BAliBASE, TreeBASE, and published studies. Subsequently, we demonstrate that AlDiScore attains an R2 of 0.89 and of 0.84 on unseen AA and DNA sequence sets extracted from the PANDIT v17 database. Finally, we show that there is no correlation between MSA difficulty and the corresponding phylogenetic difficulty of the respective MSA.

bioinformatics↗

bioRxiv2026

Unravelling the genomic and functional arsenal of Bacilli endophytes from plants with different lifestyles

Endophytic microbiomes of crop wild relatives (CWRs) adapted to extreme environments, such as halophytes, are promising sources of plant-beneficial bacteria and secondary metabolites for sustainable food production. Here, we analyzed 25 Bacilli isolates obtained from CWRs, halophytes, and other plant species in Crete, Greece. Using a hybrid Illumina-PacBio sequencing approach, we generated high-quality genomes and performed comparative genomics, phylogenetic, and pangenome analyses, complemented by in vitro assays. We identified 312 biosynthetic gene clusters (BGCs), nearly 60% of which showed no similarity to known clusters, revealing extensive unexplored biosynthetic potential. These unique BGCs may constitute an adaptive feature enabling endophytic Bacilli to colonize and interact with host plants. The isolates spanned diverse genera (Bacillus, Paenibacillus, Peribacillus, Neobacillus, Cytobacillus, Rossellomorea), including three novel species. Phenotypic assays of our isolates demonstrated high salinity tolerance (up to 17.5% w/v NaCl) and strong antagonism against major bacterial and fungal phytopathogens. Genome mining further revealed a broad array of putatively plant-beneficial traits related to growth promotion, stress adaptation, host interaction and inhibition of pathogens. Together, these findings show that Bacilli endophytes from wild and halophytic plants possess exceptional phylogenetic novelty, functional diversity, and biosynthetic capacity, providing new genomic and ecological insights into Bacilli associated with plants inhabiting extreme environments.

microbiology↗

bioRxiv2025

bigrig: A range simulator for the DEC model

Quality software tools for science are, as a necessity, rigorously tested and verified. However, there is a major challenge to testing software used for phylogenetics and similar analysis. There is a shortage of ground truth data which can be used for validation, resulting in a reliance on simulated data. This reliance on simulated data results in a second challenge: the verification of the tool which generates the simulated data. In historical biogeography, software which simulated data has exclusively been implemented on an ad-hoc basis to verify specific tools. Here, we introduce bigrig, a simulator for the DEC[+J] model of range evolution. We show that bigrig is correct with extremely rigorous statistical testing and validation, ensuring that results deviate by no more than 0.0001 with 99.999% confidence. We also show that bigrig is extremely fast, capable of generating data for trees with tens of thousands of tips in under a second.

bioinformatics↗

bioRxiv2025

Phylogenetic reconciliation supports a methanogenic ancestor of the Archaea and a derived origin for host-associated lineages

The phylogeny of the Archaea continues to be revisited and revised as new groups are discovered and phylogenetic methods improve, but key questions about their early evolution remain. It has been suggested that the root of the Archaea may lie on, or potentially within, any of three major groups - the Euryarchaeota, TACK+Asgard clade, and DPANN, the last of which includes many host-associated and genome-reduced lineages. These root hypotheses make starkly different predictions about the nature of early archaeal evolution: for example, a root on or within DPANN might suggest a small-genome and host-associated ancestor, with methanogenesis, the hallmark metabolism of the Archaea, evolving later. Here, we investigate the position of the archaeal root and the nature of the last archaeal common ancestor using a range of phylogenetic approaches, including the best available site- and branch-heterogeneous substitution models, and new gene tree-species tree reconciliation models that capture changes in rates of gene duplication, loss and transfer across the phylogeny. Our analyses converge on a narrow archaeal root region at/near the base of the Euryarchaeota, supporting hypotheses in which the Last Archaeal Common Ancestor (LACA) was a complex, free-living (hyper-)thermophilic methanogen. We recover DPANN as the sister group to TACK and Asgard archaea, and suggest that their genome evolution has been characterised by episodes of genome streamlining and expansion, driven by gene loss and transfer.

evolutionary biology↗

bioRxiv2025

A Systematic Investigation of Overfitting in Maximum Likelihood Phylogenetic Inference

Maximum Likelihood (ML) tree inference reconstructs phylogenies from Multiple Sequence Alignments (MSAs). Since MSAs are inherently noisy, ML tools may experience overfitting, whereby the inferred topology incorrectly models noise alongside the true phylogenetic signal. We statistically assess overfitting in ML tools using log-likelihood scores on unseen sites as primary metric. We deploy a 10-fold Monte Carlo cross-validation approach, partitioning 9,062 empirical and 6,342 simulated MSAs into training (80%) and testing (20%) sites. We conduct inferences using RAxML-NG, IQ-TREE, Fast-Tree, and RAxML-NG ES (a recently released Early Stopping version) on the training MSAs. We store all intermediate improved topologies and subsequently evaluate them on the testing sites. We perform a linear regression on the final segments (we use four distinct segment-window configurations) of the derived testing curves, and statistically evaluate the line slopes via the sign test. Our results indicate that ML tools do not overfit. For RAxML-NG (standard and ES) and IQ-TREE, the overall trend is non-significant for 86-98% of the empirical MSAs (across all four segment-window configurations), while for less than 1% the tools exhibit over-fitting. FastTree shows more positive trends, suggesting premature termination, especially on protein MSAs. Topological accuracy curves on simulated data further confirm that tools do not systematically diverge from the true topology. To complement these findings, we test whether strategies to mitigate overfitting can benefit ML inferences. To this end, we also benchmark a site-based holdout validation (HV) version of RAxML-NG. The results confirm that the overfitting is absent and also indicate that excluding MSA sites substantially reduces phylogenetic signal as well as accuracy.

evolutionary biology↗

bioRxiv2025

Genetic affinities between an ancient Greek colony and its metropolis: the case of Amvrakia in western Greece

BackgroundDuring the Ancient Greek colonization, Corinth established a stable network of economic and political ties, by founding colonies connecting southern Greece with the mainland of Epirus and reaching as far as the east Adriatic coast. Amvrakia, one of the main Corinthian colonies founded during the 7th century BCE, was characterized by its strong dependence on its metropolis. Here, we aim to investigate the genetic relationships between the Corinthian metropolis and the Amvrakia colony, the contribution of the local population to the founding genetic pool, as well as the demography of Amvrakia in subsequent periods. ResultsDuring its foundation in the Archaic period, Amvrakia appears to have been shaped by genetic influences from at least two different sources. The first source migrated from the Corinth territory, represented by the Archaic Tenea population and is supported via an Identity By Descent (IBD) analysis. The second source shows a direct ancestry from Late Bronze Age (LBA) / Iron Age Greece, including a local LBA population represented by the Ammotopos site located in close proximity to Amvrakia, as shown by a plethora of independent population genomics analyses. During the subsequent Classical and Hellenistic periods, the population of Amvrakia appears to have slightly differentiated, yet evidence of genetic continuity over time is observed. ConclusionsThe migration of Corinthians to Amvrakia contributed to the initial genetic pool of the colony along with the local genetic pool, indicating that the Corinthian colonization included both genetic and cultural transmission between the metropolis and its colony.

evolutionary biology↗

bioRxiv2025

Bit-Reproducible Phylogenetic Tree Inference under Varying Core-Counts via Reproducible Parallel Reduction Operators

MotivationPhylogenetic 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. ResultsWe observe that varying the degree of parallelism results in diverging phylogenetic tree searches (high level results) for over 31% out of 10 130 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 < 0.05). 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 to 12.7% (median 0.8%) on up to 768 cores. We further 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 x 106 operands across 48 to 768 cores in 19.7 to 48.61 {micro}s, thereby exhibiting a slowdown of 13 to 93% over a non-reproducible all-reduce algorithm. ReproRed outperforms the state-of-the-art reproducible all-reduction algorithm ReproBLAS (offers summation only) beyond 10 000 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 ImplementationReproRed: https://doi.org/10.5281/zenodo.15004918 (LGPL) -- Reproducible RAxML-NG version https://doi.org/10.5281/zenodo.15017407 (GPL) Contacthuebner@kit.edu Supplementary informationhttps://doi.org/10.5281/zenodo.15524754 FundingThis project received funding from the European Union via European Research Council (ERC) Horizon 2020 research and innovation grant No. 882500 and via the EU ERA Chair (HORIZON-WIDERA-2022-TALENTS-01: 2023-2028) program grant No. 101087081 (Comp-Biodiv-GR).

bioinformatics↗

bioRxiv2025

Pythia 2.0: New Data, New Prediction Model, New Features

Maximum Likelihood (ML) based phylogenetic inference is time- and resource-intensive, especially when initiating multiple independent inferences from distinct comprehensive tree topologies. Performing multiple independent inferences is often required to (sufficiently) explore the vast search space of possible unrooted binary tree topologies. Yet, these independent inferences do not necessarily converge to a single phylogeny or at least topologically highly similar trees. While for easy-to-analyze multiple sequence alignments (MSAs), one is likely to obtain a conclusive, single phylogeny, difficult-to-analyze MSAs yield topologically highly distinct, yet statistically indistinguishable tree topologies. In 2022, we proposed a compute-intensive approach to quantify the inherent difficulty of a phylogenetic analysis for a specific, given MSA, and also trained a machine-learning based prediction model called Pythia to substantially reduce the computational cost of determining the difficulty. Pythia can predict the difficulty for a given MSA with high accuracy, while being substantially faster than even a single ML tree inference. Pythia predicts the difficulty on a scale from 0 (easy) to 1 (difficult). Here, we present all improvements to Pythia that we have introduced since our initial publication in 2022. We trained a new prediction model using approximately three times more MSAs and a new type of machine learning model. We improved the runtime of two feature computations, and we also introduced two additional prediction features. Our latest version Pythia 2.0 is slightly more accurate than our initial version and is also approximately twice as fast. Finally, we also present and make available, the novel and easy-to-use command line tool PyDLG that allows to compute the ground-truth difficulty seamlessly for a given MSA. This ground-truth difficulty can be used, for instance, as a prediction target for training a new Pythia model.

bioinformatics↗

bioRxiv2025

raxtax: A k-mer-based non-Bayesian Taxonomic Classifier

MotivationTaxonomic classification in biodiversity studies is the process of assigning the anonymous sequences of a marker gene (barcode) or whole genomes (metagenomics) to a specific lineage using a reference database that contains named sequences in a known taxonomy. This classification is important for assessing the diversity of biological systems. Taxonomic classification faces two main challenges: first, accuracy is critical as errors can propagate to downstream analysis results; and second, the classification time requirements can limit study size and study design, in particular when considering the constantly growing reference databases. To address these two challenges, we introduce raxtax, an efficient, novel taxonomic classification tool for barcodes that uses common k-mers between all pairs of query and reference sequences. We also introduce two novel uncertainty scores which take into account the fundamental biases of reference databases. ResultsWe validate raxtax on three widely used empirical reference databases and show that it is 2.7-100 times faster than competing state-of-the-art tools on the largest database while being equally accurate. In particular, raxtax exhibits increasing speedups with growing query and reference sequence numbers compared to existing tools (for 100,000 and 1,000,000 query and reference sequences overall, it is 1.3 and 2.9 times faster, respectively), and therefore alleviates the taxonomic classification scalability challenge. Availability and Implementationraxtax is available at https://github.com/noahares/raxtax under a CCNC-BY-SA license. The scripts and summary metrics used in our analyses are available at https://github.com/noahares/raxtax_paper_scripts. The source code, sequence data and summarized results of the analyses are available at https://doi.org/10.5281/zenodo.15057027.

bioinformatics↗

bioRxiv2024

Much Ado About Nothing: Accelerating Maximum Likelihood Phylogenetic Inference via Early Stopping to evade (Over-)optimization

Maximum Likelihood (ML) based phylogenetic inference constitutes a challenging optimization problem. Given a set of aligned input sequences, phylogenetic inference tools strive to determine the tree topology, the branch-lengths, and the evolutionary parameters that maximize the phylogenetic likelihood function. However, there exist compelling reasons to not push optimization to its limits, by means of early, yet adequate stopping criteria. Since input sequences are typically subject to stochastic and systematic noise, one should exhibit caution regarding (over-)optimization and the inherent risk of overfitting the model to noisy input data. To this end, we propose, implement, and evaluate four statistical early stopping criteria in RAxML-NG that evade excessive and compute-intensive (over-)optimization. These generic criteria can seamlessly be integrated into other phylo-genetic inference tools while not decreasing tree accuracy. The first two criteria quantify input data-specific sampling noise to derive a stopping threshold. The third, employs the Kishino-Hasegawa (KH) test to statistically assess the significance of differences between intermediate trees before, and after major optimization steps in RAxML-NG. The optimization terminates early when improvements are insignificant. The fourth method utilizes multiple testing correction in the KH test. We show that all early stopping criteria infer trees that are statistically equivalent compared to inferences without early stopping. In conjunction with a necessary simplification of the standard RAxML-NG tree search heuristic, the average inference times on empirical and simulated datasets are [~]3.5 and [~]1.8 times faster, respectively, than for standard RAxML-NG v.1.2. The four stopping criteria have been implemented in RAxML-NG and are available as open source code under GNU GPL at https://github.com/togkousa/raxml-ng.

bioinformatics↗

bioRxiv2024

Read Length Dominates Phylogenetic Placement Accuracy of Ancient DNA Reads

A common problem when analyzing ancient DNA (aDNA) data is to identify the species which corresponds to the recovered aDNA sequence(s). The standard approach is to deploy sequence similarity based tools such as BLAST. However, as aDNA reads may frequently either stem from unsampled taxa due to extinction, it is likely that there is no exact match in any database. As a consequence, these tools may not be able to accurately place such reads in a phylogenetic context. Phylogenetic placement is a technique where a read is placed onto a specific branch of a phylogenetic reference tree, which allows for a substantially finer resolution when identifying reads. Prior applications of phylogenetic placement has deployed only on data from extant sources. Therefore, it is unclear how the aDNA damage affects phylogenetic placements applicability to aDNA data. To investigate how aDNA damage affects placement accuracy, we re-implemented a statistical model of aDNA damage. We deploy this model, along with a modified version of the existing assessment pipeline PEWO, to 7 empirical datasets with 4 leading tools: APPLES, EPA-ng, pplacer, and RAPPAS. We explore the aDNA damage parameter space via a grid search in order to identify the aDNA damage factors that exhibit the largest impact on placement accuracy. We find that the frequency of DNA backbone nicks (and consequently read length) has the by far largest impact on aDNA read placement accuracy, and that other factors, such as misincorporations, have a negligible effect on overall placement accuracy.

bioinformatics↗

bioRxiv2024

Memoization on Shared Subtrees Accelerates Computations on Genealogical Forests

The field of population genetics attempts to advance our understanding of evolutionary processes. It has applications, for example, in medical research, wildlife conservation, and - in conjunction with recent advances in ancient DNA sequencing technology - studying human migration patterns over the past few thousand years. The basic toolbox of population genetics includes genealogical tress, which describe the shared evolutionary history among individuals of the same species. They are calculated on the basis of genetic variations. However, in recombining organisms, a single tree is insufficient to describe the evolutionary history of the whole genome. Instead, a collection of correlated trees can be used, where each describes the evolutionary history of a consecutive region of the genome. The current corresponding state of-the-art data structure, tree sequences, compresses these genealogical trees via edit operations when moving from one tree to the next along the genome instead of storing the full, often redundant, description for each tree. We propose a new data structure, genealogical forests, which compresses the set of genealogical trees into a DAG. In this DAG identical subtrees that are shared across the input trees are encoded only once, thereby allowing for straight-forward memoization of intermediate results. Additionally, we provide a C++ implementation of our proposed data structure, called gfkit, which is 2.1 to 11.2 (median 4.0) times faster than the state-of-the-art tool on empirical and simulated datasets at computing important population genetics statistics such as the Allele Frequency Spectrum, Pattersons f, the Fixation Index, Tajimas D, pairwise Lowest Common Ancestors, and others. On Lowest Common Ancestor queries with more than two samples as input, gfkit scales asymptotically better than the state-of-the-art, and is thus up to 990 times faster. In conclusion, our proposed data structure compresses genealogical trees by storing shared subtrees only once, thereby enabling straight-forward memoization of intermediate results, yielding a substantial runtime reduction and a potentially more intuitive data representation over the state-of-the-art. Our improvements will boost the development of novel analyses and models in the field of population genetics and increases scalability to ever-growing genomic datasets. 2012 ACM Subject ClassificationApplied computing [->] Computational genomics; Applied computing [->] Molecular sequence analysis; Applied computing [->] Bioinformatics; Applied computing [->] Population genetics

bioinformatics↗

bioRxiv2024

Pandora: A Tool to Estimate Dimensionality Reduction Stability of Genotype Data

MotivationGenotype datasets typically contain a large number of single nucleotide polymorphisms for a comparatively small number of individuals. To identify similarities between individuals and to infer an individuals origin or membership to a population, dimensionality reduction techniques are routinely deployed. However, inherent (technical) difficulties such as missing or noisy data need to be accounted for when analyzing a lower dimensional representation of genotype data, and the intrinsic uncertainty of such analyses should be reported in all studies. However, to date, there exists no stability assessment technique for genotype data that can estimate this uncertainty. ResultHere, we present Pandora, a stability estimation framework for genotype data based on bootstrapping. Pandora computes an overall score to quantify the stability of the entire embedding, infers per-individual support values, and also deploys a k-means clustering approach to assess the uncertainty of assignments to potential cultural groups. Using published empirical and simulated datasets, we demonstrate the usage and utility of Pandora for studies that rely on dimensionality reduction techniques. Availability and ImplementationPandora is available on GitHub https://github.com/tschuelia/Pandora. Contactjulia.haag@h-its.org Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2024

Predicting Phylogenetic Bootstrap Values viaMachine Learning

SummaryEstimating the statistical robustness of the inferred tree(s) constitutes an integral part of most phylogenetic analyses. Commonly, one computes and assigns a branch support value to each inner branch of the inferred phylogeny. The most widely used method for calculating branch support on trees inferred under Maximum Likelihood (ML) is the Standard, non-parametric Felsenstein Bootstrap Support (SBS). Due to the high computational cost of the SBS, a plethora of methods has been developed to approximate it, for instance, via the Rapid Bootstrap (RB) algorithm. There have also been attempts to devise faster, alternative support measures, such as the SH-aLRT (Shimodaira-Hasegawalike approximate Likelihood Ratio Test) or the UltraFast Bootstrap 2 (UFBoot2) method. Those faster alternatives exhibit some limitations, such as the need to assess model violations (UFBoot2) or meaningless low branch support intervals (SH-aLRT). Here, we present the Educated Bootstrap Guesser (EBG), a machine learning-based tool that predicts SBS branch support values for a given input phylogeny. EBG is on average 9.4 ({sigma} = 5.5) times faster than UFBoot2. EBG-based SBS estimates exhibit a median absolute error of 5 when predicting SBS values between 0 and 100. Furthermore, EBG also provides uncertainty measures for all per-branch SBS predictions and thereby allows for a more rigorous and careful interpretation. EBG can predict SBS support values on a phylogeny comprising 1654 SARS-CoV2 genome sequences within 3 hours on a mid-class laptop. EBG is available under GNU GPL3. Data and Code Availabilitygithub.com/wiegertj/EBG github.com/wiegertj/EBG-train Contactjulius-wiegert@web.de

bioinformatics↗