Computational Molecular Evolution

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The Computational Molecular Evolution (CME) group focuses on developing algorithms, computer architectures, and high-performance computing solutions for bioinformatics.

SEEK ID: https://publications.h-its.org/projects/13

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Research Group created: 7th Nov 2017

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Showing 5 out of a possible 15 People Advanced People list for this Research Group with search and filtering

Pierre Barbera

Research Groups: Computational Molecular Evolution

Institutions: Heidelberg Institute for Theoretical Studies

Ben Bettisworth

Research Groups: Computational Molecular Evolution

Institutions: Heidelberg Institute for Theoretical Studies

Julia Haag

Research Groups: Computational Molecular Evolution

Institutions: Heidelberg Institute for Theoretical Studies

Luise Häuser

Research Groups: Computational Molecular Evolution

Institutions: Heidelberg Institute for Theoretical Studies

Dimitri Höhler

Research Groups: Computational Molecular Evolution

Institutions: Heidelberg Institute for Theoretical Studies

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Heidelberg Institute for Theoretical Studies

Country: Germany

City: Heidelberg

Web page: https://www.h-its.org

Showing 5 out of a possible 127 Publications Advanced Publications list for this Research Group with search and filtering

AleRax: A tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer, and loss

Computational Molecular Evolution

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ABSTRACT Motivation Genomes are a rich source of information on the pattern and process of evolution across biological scales. How best to make use of that information is an active area of research in …at information is an active area of research in phylogenetics. Ideally, phylogenetic methods should not only model substitutions along gene trees, which explain differences between homologous gene sequences, but also the processes that generate the gene trees themselves along a shared species tree. To conduct accurate inferences, one needs to account for uncertainty at both levels, that is, in gene trees estimated from inherently short sequences and in their diverse evolutionary histories along a shared species tree. Results We present AleRax, a software that can infer reconciled gene trees together with a shared species tree using a simple, yet powerful, probabilistic model of gene duplication, transfer, and loss. A key feature of AleRax is its ability to account for uncertainty in the gene tree and its reconciliation by using an efficient approximation to calculate the joint phylogenetic-reconciliation likelihood and sample reconciled gene trees accordingly. Simulations and analyses of empirical data show that AleRax is one order of magnitude faster than competing gene tree inference tools while attaining the same accuracy. It is consistently more robust than species tree inference methods such as SpeciesRax and ASTRAL-Pro 2 under gene tree uncertainty. Finally, AleRax can process multiple gene families in parallel thereby allowing users to compare competing phylogenetic hypotheses and estimate model parameters, such as DTL probabilities for genome-scale datasets with hundreds of taxa Availability and Implementation GNU GPL at https://github.com/BenoitMorel/AleRax and data are made available at https://cme.h-its.org/exelixis/material/alerax_data.tar.gz . Contact Benoit.Morel@h-its.org Supplementary information Supplementary material is available.

Authors: Benoit Morel, Tom A. Williams, Alexandros Stamatakis, Gergely J. Szöllősi

Date Published: 7th Oct 2023

Publication Type: Journal

DOI: 10.1101/2023.10.06.561091

Citation: biorxiv;2023.10.06.561091v2,[Preprint]

Created: 2nd Jan 2024 at 18:29, Last updated: 5th Mar 2024 at 21:25

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

Computational Molecular Evolution

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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

DOI: 10.1093/molbev/msad227

Citation: Molecular Biology and Evolution 40(10),msad227

Created: 2nd Jan 2024 at 18:28, Last updated: 5th Mar 2024 at 21:25

Interpreting phylogenetic placements for taxonomic assignment of environmental DNA

Computational Molecular Evolution

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Abstract Taxonomic assignment of operational taxonomic units (OTUs) is an important bioinformatics step in analyzing environmental sequencing data. Pairwise alignment and phylogenetic‐placement methods …

Authors: Isabelle Ewers, Lubomír Rajter, Lucas Czech, Frédéric Mahé, Alexandros Stamatakis, Micah Dunthorn

Date Published: 1st Sep 2023

Publication Type: Journal

DOI: 10.1111/jeu.12990

Citation: J Eukaryotic Microbiology 70(5),e12990

Created: 2nd Jan 2024 at 18:26, Last updated: 5th Mar 2024 at 21:25

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

Computational Molecular Evolution

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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

DOI: 10.1101/2023.07.11.548509

Citation: biorxiv;2023.07.11.548509v2,[Preprint]

Created: 2nd Jan 2024 at 18:25, Last updated: 5th Mar 2024 at 21:25

Parallel Inference of Phylogenetic Stands with Gentrius

Computational Molecular Evolution