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13 Publications visible to you, out of a total of 13
Use Cases of Predictive Modeling for Phylogenetic Inference and Placements

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In this work, we present two distinct applications of predictive modeling within the domain of phylogenetic inference and placement. Phylogenetic placements aim to place new entities into a given … phylogenetic tree. While there exist efficient implementations for producing phylogenetic placements, the underlying reasons why particular placements are more difficult to perform than others are unknown. In the first use case, we focus on the prediction of the difficulty of those phylogenetic placements. We developed Bold Assertor of Difficulty (BAD). BAD can predict the placement difficulty between 0 (easy) and 1 (hard) with high accuracy. On a set of 3000 metagenomic placements, we obtain a mean absolute error of 0.13. BAD can help biologists understand the challenges associated with placing specific sequences into a reference phylogeny during metagenomic studies based on SHapley Additive exPlanations (SHAP) explanations. Estimating the statistical robustness of the inferred phylogenetic tree 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 is the Standard, non parametric Felsenstein Bootstrap Support (SBS). The SBS method is computationally costly, leading to the development of alternative approaches such as Rapid Bootstrap and UltraFast Bootstrap 2 (UFBoot2). The second use case of this work is concerned with the fast machine learning-based approxi mation of those SBS values. Our SBS predictor, Educated Bootstrap Guesser (EBG), is on average 9.4 (𝜎 = 5.5) times faster than the major competitor UFBoot2 and provides an SBS estimate with a median absolute error of 5 when predicting SBS values between 0 and 10

Authors: Julius Wiegert, Julia Haag, Dimitri Höhler, Alexandros Stamatakis

Date Published: 7th Apr 2024

Publication Type: Master's Thesis

Complexity of avian evolution revealed by family-level genomes

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Despite tremendous efforts in the past decades, relationships among main avian lineages remain heavily debated without a clear resolution. Discrepancies have been attributed to diversity of species … sampled, phylogenetic method, and the choice of genomic regions 1–3. Here, we address these issues by analyzing genomes of 363 bird species 4 (218 taxonomic families, 92% of total). Using intergenic regions and coalescent methods, we present a well-supported tree but also a remarkable degree of discordance. The tree confirms that Neoaves experienced rapid radiation at or near the Cretaceous–Paleogene (K–Pg) boundary. Sufficient loci rather than extensive taxon sampling were more effective in resolving difficult nodes. Remaining recalcitrant nodes involve species that challenge modeling due to extreme GC content, variable substitution rates, incomplete lineage sorting, or complex evolutionary events such as ancient hybridization. Assessment of the impacts of different genomic partitions showed high heterogeneity across the genome. We discovered sharp increases in effective population size, substitution rates, and relative brain size following the K–Pg extinction event, supporting the hypothesis that emerging ecological opportunities catalyzed the diversification of modern birds. The resulting phylogenetic estimate offers novel insights into the rapid radiation of modern birds and provides a taxon-rich backbone tree for future comparative studies.

Authors: Josefin Stiller, Shaohong Feng, Al-Aabid Chowdhury, Iker Rivas-González, David A. Duchêne, Qi Fang, Yuan Deng, Alexey Kozlov, Alexandros Stamatakis, Santiago Claramunt, Jacqueline M. T. Nguyen, Simon Y. W. Ho, Brant C. Faircloth, Julia Haag, Peter Houde, Joel Cracraft, Metin Balaban, Uyen Mai, Guangji Chen, Rongsheng Gao, Chengran Zhou, Yulong Xie, Zijian Huang, Zhen Cao, Zhi Yan, Huw A. Ogilvie, Luay Nakhleh, Bent Lindow, Benoit Morel, Jon Fjeldså, Peter A. Hosner, Rute R. da Fonseca, Bent Petersen, Joseph A. Tobias, Tamás Székely, Jonathan David Kennedy, Andrew Hart Reeve, Andras Liker, Martin Stervander, Agostinho Antunes, Dieter Thomas Tietze, Mads Bertelsen, Fumin Lei, Carsten Rahbek, Gary R. Graves, Mikkel H. Schierup, Tandy Warnow, Edward L. Braun, M. Thomas P. Gilbert, Erich D. Jarvis, Siavash Mirarab, Guojie Zhang

Date Published: 1st Apr 2024

Publication Type: Journal

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

Abstract (Expand)

Motivation: Genotype 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 individual’s origin or membership to a cultural group, 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 uncertainty of such an analysis should be reported in all studies. However, to date, there exists no stability estimation technique for genotype data that can estimate this uncertainty. Results: Here, 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, perindividual support values, and deploys a k-means clustering approach to assess the uncertainty of assignments to potential cultural groups. In addition to this bootstrap-based stability estimation, Pandora offers a sliding-window stability estimation for whole-genome data. Using published empirical and simulated datasets, we demonstrate the usage and utility of Pandora for studies that rely on dimensionality reduction techniques. Data and Code: Availability Pandora is available on GitHub https://github.com/tschuelia/Pandora. All Python scripts and data to reproduce our results are available on GitHub https://github.com/tschuelia/PandoraPaper.

Authors: Julia Haag, Alexander I. Jordan, Alexandros Stamatakis

Date Published: 15th Mar 2024

Publication Type: Journal

Predicting Phylogenetic Bootstrap Values via Machine Learning

Abstract (Expand)

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–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 (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.

Authors: Julius Wiegert, Dimitri Höhler, Julia Haag, Alexandros Stamatakis

Date Published: 6th Mar 2024

Publication Type: Journal

Adaptive RAxML-NG: Accelerating Phylogenetic Inference under Maximum Likelihood using Dataset Difficulty

Abstract (Expand)

Abstract Phylogenetic inferences under the maximum likelihood criterion deploy heuristic tree search strategies to explore the vast search space. Depending on the input dataset, searches from different …t, searches from different starting trees might all converge to a single tree topology. Often, though, distinct searches infer multiple topologies with large log-likelihood score differences or yield topologically highly distinct, yet almost equally likely, trees. Recently, Haag et al. introduced an approach to quantify, and implemented machine learning methods to predict, the dataset difficulty with respect to phylogenetic inference. Easy multiple sequence alignments (MSAs) exhibit a single likelihood peak on their likelihood surface, associated with a single tree topology to which most, if not all, independent searches rapidly converge. As difficulty increases, multiple locally optimal likelihood peaks emerge, yet from highly distinct topologies. To make use of this information, we introduce and implement an adaptive tree search heuristic in RAxML-NG, which modifies the thoroughness of the tree search strategy as a function of the predicted difficulty. Our adaptive strategy is based upon three observations. First, on easy datasets, searches converge rapidly and can hence be terminated at an earlier stage. Second, overanalyzing difficult datasets is hopeless, and thus it suffices to quickly infer only one of the numerous almost equally likely topologies to reduce overall execution time. Third, more extensive searches are justified and required on datasets with intermediate difficulty. While the likelihood surface exhibits multiple locally optimal peaks in this case, a small proportion of them is significantly better. Our experimental results for the adaptive heuristic on 9,515 empirical and 5,000 simulated datasets with varying difficulty exhibit substantial speedups, especially on easy and difficult datasets (53% of total MSAs), where we observe average speedups of more than 10×. Further, approximately 94% of the inferred trees using the adaptive strategy are statistically indistinguishable from the trees inferred under the standard strategy (RAxML-NG).

Authors: Anastasis Togkousidis, Oleksiy M Kozlov, Julia Haag, Dimitri Höhler, Alexandros Stamatakis

Date Published: 1st Oct 2023

Publication Type: Journal

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

Abstract (Expand)

Abstract Motivation Simulating Multiple Sequence Alignments (MSAs) using probabilistic models of sequence evolution plays an important role in the evaluation of phylogenetic inference tools, and is …luation 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. Results Our 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 Availability All 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 . Contact julia.haag@h-its.org

Authors: Johanna Trost, Julia Haag, Dimitri Höhler, Laurent Jacob, Alexandros Stamatakis, Bastien Boussau

Date Published: 12th Jul 2023

Publication Type: Journal

Quantitative Analysis and Characterization of Natural Language Evolution Datasets

Abstract (Expand)

Methods for phylogenetic inference have been developed mainly for the reconstruction of evolutionary relationships of species based on biological sequence data. However, these methods are also made use … of in linguistics for inferring phylogenies concerning the evolution of natural languages. In the scope of this thesis, we examine the corresponding linguistic input data. We conduct a case study on an exemplary morphosyntactic data set, examining various methods to analyze the signal it contains and to eliminate geographical information the data may include. Further, we perform analyses on numerous linguistic data sets collected from various sources and assembled in a database. We compare these data sets to morphological data from biology, considering differences in the behavior of phylogenetic inferences with RAxML-NG. Additionally, we investigate how it impacts the tree inferences, whether we represent a data set by a binary or by a multi-valued MSA. We study how to model subjectivity related with synonym selection in cognate data. We present probabilistic MSAs as a possible solution and show on an example data set that this might be an appropriate approach

Authors: Luise Häuser, Julia Haag, Alexandros Stamatakis

Date Published: 17th Jun 2023

Publication Type: Master's Thesis

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