Simulations of sequence evolution: how (un)realistic they are and why

Trost, J. Ronald; Haag, Julia; Höhler, Dimitri; Nesterenko, Luca; Jacob, Laurent; Stamatakis, Alexandros; Boussau, Bastien

doi:10.1101/2023.07.11.548509

Cited by 6 publications

(4 citation statements)

References 53 publications

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“…Among the empirical datasets, 575 are DNA datasets and 150 are AA MSAs, whereas all simulated MSAs are DNA datasets, in order to reduce the CO 2 footprint. We sampled the empirical MSAs from the TreeBASE databse (Piel et al, 2009) and the simulated MSAs from the datasets used in a recent benchmark study conducted by our group and colleagues (Trost et al, 2024). The selected empirical and simulated MSAs capture the full spectrum of difficulty scores predicted by the Pythia tool (Haag et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

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

Togkousidis,

Stamatakis,

Gascuel

2024

Preprint

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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 treesbefore, andaftermajor 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 athttps://github.com/togkousa/raxml-ng.

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Section: Resultsmentioning

confidence: 99%

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

Togkousidis,

Stamatakis,

Gascuel

2024

Preprint

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“…the proportion of gaps or alternative measures/predictions of phylogenetic signal). To this end, simulating MSAs with specific attributes could constitute a way forward, albeit simulations still tend to be unrealistic ( Trost et al 2023 ). However, such a more thorough exploration should be carefully considered, as performing a vast amount of tree inferences is computationally expensive.…”

Section: Discussionmentioning

confidence: 99%

The Free Lunch is not over yet—systematic exploration of numerical thresholds in maximum likelihood phylogenetic inference

Haag,

Hübner,

Kozlov

et al. 2023

Bioinformatics Advances

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Maximum Likelihood (ML) is a widely used phylogenetic inference method. ML implementations heavily rely on numerical optimization routines that use internal numerical thresholds to determine convergence. We systematically analyze the impact of these threshold settings on the log-likelihood and runtimes for ML tree inferences with RAxML-NG, IQ-TREE, and FastTree on empirical datasets. We provide empirical evidence that we can substantially accelerate tree inferences with RAxML-NG and IQ-TREE by changing the default values of two such numerical thresholds. At the same time, altering these settings does not significantly impact the quality of the inferred trees. We further show that increasing both thresholds accelerates the RAxML-NG bootstrap without influencing the resulting support values. For RAxML-NG, increasing the likelihood thresholds ϵLnL and ϵbrlen to 10 and 103 respectively results in an average tree inference speedup of 1.9 ±0.6 on Data collection 1, 1.8 ±1.1 on Data collection 2, and 1.9 ±0.8 on Data collection 2 for the RAxML-NG bootstrap compared to the runtime under the current default setting. Increasing the likelihood threshold ϵLnL to 10 in IQ-TREE results in an average tree inference speedup of 1.3 ±0.4 on Data collection 1 and 1.3 ±0.9 on Data collection 2.

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“…Trost et al [32] demonstrate, that machine learning algorithms can easily distinguish between simulated and empirical MSAs with high accuracy and conclude that sequence simulations do not fully capture all characteristics of empirical MSAs. Consequently, we exclusively use empirical MSAs to train EBG.…”

Section: Training Datamentioning

confidence: 99%

Predicting Phylogenetic Bootstrap Values via Machine Learning

Wiegert,

Höhler,

Haag

et al. 2024

Preprint

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Estimating 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-Hasegawa-like 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 (σ = 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.

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Simulations of sequence evolution: how (un)realistic they are and why

Cited by 6 publications

References 53 publications

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

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

The Free Lunch is not over yet—systematic exploration of numerical thresholds in maximum likelihood phylogenetic inference

Predicting Phylogenetic Bootstrap Values via Machine Learning

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