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van Iersel L, Moulton V, Murakami Y. Polynomial invariants for cactuses. INFORM PROCESS LETT 2023. [DOI: 10.1016/j.ipl.2023.106394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Barzilai LP, Schrago CG. Signatures of natural selection in tree topology shape of serially sampled viral phylogenies. Mol Phylogenet Evol 2023; 183:107776. [PMID: 36990305 DOI: 10.1016/j.ympev.2023.107776] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/24/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023]
Abstract
Tree shape metrics can be computed fast for trees of any size, which makes them promising alternatives to intensive statistical methods and parameter-rich evolutionary models in the era of massive data availability. Previous studies have demonstrated their effectiveness in unveiling important parameters in viral evolutionary dynamics, although the impact of natural selection on the shape of tree topologies has not been thoroughly investigated. We carried out a forward-time and individual-based simulation to investigate whether tree shape metrics of several kinds could predict the selection regime employed to generate the data. To examine the impact of the genetic diversity of the founder viral population, simulations were run under two opposing starting configurations of the genetic diversity of the infecting viral population. We found that four evolutionary regimes, namely, negative, positive, and frequency-dependent selection, as well as neutral evolution, were successfully distinguished by tree topology shape metrics. Two metrics from the Laplacian spectral density profile (principal eigenvalue and peakedness) and the number of cherries were the most informative for indicating selection type. The genetic diversity of the founder population had an impact on differentiating evolutionary scenarios. Tree imbalance, which has been frequently associated with the action of natural selection on intrahost viral diversity, was also characteristic of neutrally evolving serially sampled data. Metrics calculated from empirical analysis of HIV datasets indicated that most tree topologies exhibited shapes closer to the frequency-dependent selection or neutral evolution regimes.
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Voznica J, Zhukova A, Boskova V, Saulnier E, Lemoine F, Moslonka-Lefebvre M, Gascuel O. Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks. Nat Commun 2022; 13:3896. [PMID: 35794110 PMCID: PMC9258765 DOI: 10.1038/s41467-022-31511-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/21/2022] [Indexed: 12/03/2022] Open
Abstract
Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available on github.com/evolbioinfo/phylodeep .
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Affiliation(s)
- J Voznica
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France.
- Université de Paris, Paris, France.
- Institut de Biologie de l'École Normale Supérieure, Ecole Normale Supérieure, CNRS, INSERM, Université Paris Sciences et Lettres, Paris, France.
| | - A Zhukova
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France.
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, France.
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Antibiotic Evasion, Paris, France.
- Université Paris-Saclay, UVSQ, Inserm, CESP, Villejuif, France.
| | - V Boskova
- Center for Integrative Bioinformatics Vienna, Max Perutz Labs, University of Vienna and Medical University of Vienna, Vienna, Austria
| | - E Saulnier
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France
| | - F Lemoine
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, France
| | - M Moslonka-Lefebvre
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France
| | - O Gascuel
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France.
- Institut de Systématique, Evolution, Biodiversité (UMR 7205 - CNRS, Muséum National d'Histoire Naturelle, SU, EPHE, UA), Paris, France.
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