Biology subjects

Hoehler, D.

Publications and source records attributed to Hoehler, D..

6 recordsLinked to original sources

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↗

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↗

bioRxiv2023

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

MotivationSimulating Multiple Sequence Alignments (MSAs) using probabilistic models of sequence evolution plays an important role in the evaluation of phylogenetic inference tools, and is crucial to the development of novel learning-based approaches for phylogenetic reconstruction, for instance, neural networks. These models and the resulting simulated data need to be as realistic as possible to be indicative of the performance of the developed tools on empirical data and to ensure that neural networks trained on simulations perform well on empirical data. Over the years, numerous models of evolution have been published with the goal to represent as faithfully as possible the sequence evolution process and thus simulate empirical-like data. In this study, we simulated DNA and protein MSAs under increasingly complex models of evolution with and without insertion/deletion (indel) events using a state-of-the-art sequence simulator. We assessed their realism by quantifying how accurately supervised learning methods are able to predict whether a given MSA is simulated or empirical. ResultsOur results show that we can distinguish between empirical and simulated MSAs with high accuracy using two distinct and independently developed classification approaches across all tested models of sequence evolution. Our findings suggest that the current state-of-the-art models fail to accurately replicate several aspects of empirical MSAs, including site-wise rates as well as amino acid and nucleotide composition. Data and Code AvailabilityAll simulated and empirical MSAs, as well as all analysis results, are available at https://cme.h-its.org/exelixis/material/simulation_study.tar.gz. All scripts required to reproduce our results are available at https://github.com/tschuelia/SimulationStudy and https://github.com/JohannaTrost/seqsharp. Contactjulia.haag@h-its.org

bioinformatics↗

bioRxiv2022

A representative Performance Assessment of Maximum Likelihood based Phylogenetic Inference Tools

SummaryThe evaluation of phylogenetic inference tools is commonly conducted on simulated and empirical sequence data alignments. An open question is how representative these alignments are with respect to those, commonly analyzed by users. Based upon the RAxMLGrove database, it is now possible to simulate DNA sequences based on more than 70, 000 representative RAxML and RAxML-NG tree inferences on empirical datasets conducted on the RAxML web servers. This allows to assess the phylogenetic tree inference accuracy of various inference tools based on realistic and representative simulated DNA alignments. We simulated 20, 000 MSAs based on representative datasets (in terms of signal strength) from RAxMLGrove, and used 5, 000 datasets from the TreeBASE database, to assess the inference accuracy of FastTree2, IQ-TREE2, and RAxML-NG. We find that on quantifiably difficult-to-analyze MSAs all of the analysed tools perform poorly, such that the quicker FastTree2, can constitute a viable alternative to infer trees. We also find, that there are substantial differences between accuracy results on simulated and empirical data, despite the fact that a substantial effort was undertaken to simulate sequences under as realistic as possible settings. ContactDimitri Hohler, dimitri.hoehler@h-its.org

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bioRxiv2021

RAxML Grove: An empirical Phylogenetic Tree Database

The assessment of novel phylogenetic models and inference methods is routinely being conducted via experiments on simulated as well as empirical data. When generating synthetic data it is often unclear how to set simulation parameters for the models and generate trees that appropriately reflect empirical model parameter distributions and tree shapes. As a solution, we present and make available a new database called RAxML Grove currently comprising more than 60,000 inferred trees and respective model parameter estimates from fully anonymized empirical data sets that were analyzed using RAxML (1) and RAxML-NG (2) on two web servers. We also describe and make available two simple applications of RAxML Grove to exemplify its usage and highlight its utility for designing realistic simulation studies and analyzing empirical model parameter and tree shape distributions. RAxML Grove is freely available at https://github.com/angtft/RAxMLGrove.

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bioRxiv2020

SoftWipe - a tool and benchmark to assess scientific software quality

Scientific software from all areas of scientific research is pivotal to obtaining novel insights. Yet the quality of scientific software is rarely assessed, even though it might lead to incorrect scientific results in the worst case. Therefore, we have developed an open source tool and benchmark called SoftWipe, that provides a relative software quality ranking of 51 computational tools from diverse research areas. SoftWipe can be used in the review process of software papers and to inform the scientific software selection process.

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