دورية أكاديمية
CMAPLE: Efficient Phylogenetic Inference in the Pandemic Era.
| العنوان: | CMAPLE: Efficient Phylogenetic Inference in the Pandemic Era. |
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| المؤلفون: | Ly-Trong N; School of Computing, College of Engineering, Computing and Cybernetics, Australian National University, Canberra, ACT 2600, Australia., Bielow C; Bioinformatics Solution Center, Freie Universität Berlin, 14195 Berlin, Germany., De Maio N; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK., Minh BQ; School of Computing, College of Engineering, Computing and Cybernetics, Australian National University, Canberra, ACT 2600, Australia. |
| المصدر: | Molecular biology and evolution [Mol Biol Evol] 2024 Jul 03; Vol. 41 (7). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Oxford University Press Country of Publication: United States NLM ID: 8501455 Publication Model: Print Cited Medium: Internet ISSN: 1537-1719 (Electronic) Linking ISSN: 07374038 NLM ISO Abbreviation: Mol Biol Evol Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2003- : New York, NY : Oxford University Press Original Publication: [Chicago, Ill.] : University of Chicago Press, [c1983- |
| مواضيع طبية MeSH: | Phylogeny* , Software*, Algorithms ; Pandemics ; Likelihood Functions ; Humans |
| مستخلص: | We have recently introduced MAPLE (MAximum Parsimonious Likelihood Estimation), a new pandemic-scale phylogenetic inference method exclusively designed for genomic epidemiology. In response to the need for enhancing MAPLE's performance and scalability, here we present two key components: (i) CMAPLE software, a highly optimized C++ reimplementation of MAPLE with many new features and advancements, and (ii) CMAPLE library, a suite of application programming interfaces to facilitate the integration of the CMAPLE algorithm into existing phylogenetic inference packages. Notably, we have successfully integrated CMAPLE into the widely used IQ-TREE 2 software, enabling its rapid adoption in the scientific community. These advancements serve as a vital step toward better preparedness for future pandemics, offering researchers powerful tools for large-scale pathogen genomic analysis. (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.) |
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| معلومات مُعتمدة: | EOSS4-0000000312 Chan-Zuckerberg Initiative grant for open-source software for science; DP200103151 Australian Research Council Discovery grant; 735923LPI Moore-Simons Foundation grant; VGRS20042M Vingroup Science and Technology Scholarship |
| فهرسة مساهمة: | Keywords: epidemiology; maximum likelihood; models of sequence evolution; phylogenetics; phylogenomics |
| تواريخ الأحداث: | Date Created: 20240627 Date Completed: 20240710 Latest Revision: 20240711 |
| رمز التحديث: | 20240711 |
| مُعرف محوري في PubMed: | PMC11232695 |
| DOI: | 10.1093/molbev/msae134 |
| PMID: | 38934791 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1537-1719 |
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| DOI: | 10.1093/molbev/msae134 |