Julia Haag

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About Julia Haag:

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SEEK ID: https://publications.h-its.org/people/168

Location: Germany

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Joined: 17th Jan 2022

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Computational Molecular Evolution

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

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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 11 Publications Advanced Publications list for this Person with search and filtering

Complexity of avian evolution revealed by family-level genomes

Computational Molecular Evolution

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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 …

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

DOI: 10.1038/s41586-024-07323-1

Citation: Nature

Created: 23rd Apr 2024 at 11:12, Last updated: 23rd Apr 2024 at 11:13

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

Computational Molecular Evolution

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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 …

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

Date Published: 15th Mar 2024

Publication Type: Journal

DOI: 10.1101/2024.03.14.584962

Citation: biorxiv;2024.03.14.584962v1,[Preprint]

Created: 23rd Apr 2024 at 11:14, Last updated: 23rd Apr 2024 at 11:15

Predicting Phylogenetic Bootstrap Values via Machine Learning

Computational Molecular Evolution

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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 …

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

Date Published: 6th Mar 2024

Publication Type: Journal

DOI: 10.1101/2024.03.04.583288

Citation: biorxiv;2024.03.04.583288v1,[Preprint]

Created: 23rd Apr 2024 at 11:16, Last updated: 23rd Apr 2024 at 11:17

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

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

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