matOptimize: A parallel tree optimization method enables online phylogenetics for SARS-CoV-2
| Autor: | Ye, Cheng, Thornlow, Bryan, Hinrichs, Angie, Kramer, Alexander, Michandani, Cade, Torvi, Devika, Lanfear, Rob, Corbett-Detig, Russell, Turakhia, Yatish |
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| Přispěvatelé: | Schwartz, Russell |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Statistics and Probability
Online phylogenetics SARS-CoV-2 Bioinformatics COVID-19 Biological Sciences Biochemistry Tree optimization Mathematical Sciences Computer Science Applications Computational Mathematics Good Health and Well Being Computational Theory and Mathematics Information and Computing Sciences Humans Pandemics Molecular Biology Phylogeny Software SARS-CoV-2 phylogenetics |
| Zdroj: | Bioinformatics (Oxford, England), vol 38, iss 15 |
| DOI: | 10.5281/zenodo.6709905 |
| Popis: | Motivation Phylogenetic tree optimization is necessary for precise analysis of evolutionary and transmission dynamics, but existing tools are inadequate for handling the scale and pace of data produced during the coronavirus disease 2019 (COVID-19) pandemic. One transformative approach, online phylogenetics, aims to incrementally add samples to an ever-growing phylogeny, but there are no previously existing approaches that can efficiently optimize this vast phylogeny under the time constraints of the pandemic. Results Here, we present matOptimize, a fast and memory-efficient phylogenetic tree optimization tool based on parsimony that can be parallelized across multiple CPU threads and nodes, and provides orders of magnitude improvement in runtime and peak memory usage compared to existing state-of-the-art methods. We have developed this method particularly to address the pressing need during the COVID-19 pandemic for daily maintenance and optimization of a comprehensive SARS-CoV-2 phylogeny. matOptimize is currently helping refine on a daily basis possibly the largest-ever phylogenetic tree, containing millions of SARS-CoV-2 sequences. Availability and implementation The matOptimize code is freely available as part of the UShER package (https://github.com/yatisht/usher) and can also be installed via bioconda (https://bioconda.github.io/recipes/usher/README.html). All scripts we used to perform the experiments in this manuscript are available at https://github.com/yceh/matOptimize-experiments. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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