Alexey Kozlov

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About Alexey Kozlov:

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SEEK ID: https://publications.h-its.org/people/54

Location: Germany

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

EcoFreq: Compute with Cheaper, Cleaner Energy via Carbon-Aware Power Scaling

Computational Molecular Evolution

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High-performance computing (HPC) constitutes an energy-hungry endeavor, and any efficiency gains via hardware and software advances are quickly (over-)compensated by increased consumption (rebound …

Authors: Oleksiy M. Kozlov, Alexandros Stamatakis

Date Published: 1st May 2024

Publication Type: Proceedings

DOI: 10.23919/ISC.2024.10528928

Citation: ISC High Performance 2024 Research Paper Proceedings (39th International Conference),pp.1-12,IEEE

Created: 9th Jan 2025 at 10:21, Last updated: 9th Jan 2025 at 10:22

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

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

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

Computational Molecular Evolution

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Abstract Summary Maximum likelihood (ML) is a widely used phylogenetic inference method. ML implementations heavily rely on numerical optimization routines that use internal numerical thresholds to …

Authors: Julia Haag, Lukas Hübner, Alexey M Kozlov, Alexandros Stamatakis

Date Published: 2023

Publication Type: Journal

DOI: 10.1093/bioadv/vbad124

Citation: Bioinformatics Advances 3(1),vbad124

Created: 2nd Jan 2024 at 18:27, Last updated: 5th Mar 2024 at 21:25

Resource-aware Research on Universe and Matter: Call-to-Action in Digital Transformation

Computational Molecular Evolution

Abstract

Not specified

Authors: Ben Bruers, Marilyn Cruces, Markus Demleitner, Guenter Duckeck, Michael Düren, Niclas Eich, Torsten Enßlin, Johannes Erdmann, Martin Erdmann, Peter Fackeldey, Christian Felder, Benjamin Fischer, Stefan Fröse, Stefan Funk, Martin Gasthuber, Andrew Grimshaw, Daniela Hadasch, Moritz Hannemann, Alexander Kappes, Raphael Kleinemühl, Oleksiy M. Kozlov, Thomas Kuhr, Michael Lupberger, Simon Neuhaus, Pardis Niknejadi, Judith Reindl, Daniel Schindler, Astrid Schneidewind, Frank Schreiber, Markus Schumacher, Kilian Schwarz, Achim Streit, R. Florian von Cube, Rod Walker, Cyrus Walther, Sebastian Wozniewski, Kai Zhou

Date Published: 2023

Publication Type: Journal

Citation: arXiv,physics.comp-ph,2311.01169

Created: 8th Jan 2024 at 16:08, Last updated: 5th Mar 2024 at 21:25

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