吉祥物-天际线整合了人口和迁移动态,以加强系统地理重建。

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-09-26 eCollection Date: 2025-09-01 DOI:10.1371/journal.pcbi.1013421
Nicola F Müller, Remco R Bouckaert, Chieh-Hsi Wu, Trevor Bedford
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引用次数: 0

摘要

传染病的传播受到空间和时间方面的影响,例如宿主人口结构或传播率或受感染个体数量随时间的变化。这些时空动力学印记在病原体的基因组中,并且可以使用系统动力学方法从这些基因组中恢复。然而,系统动力学方法通常量化时间或空间传输动力学,这导致不明确的偏差,因为如果没有另一个,可能无法推断出一个。在这里,我们通过引入一种结构化的聚结天际线方法来解决这一挑战,即吉祥物-天际线方法,该方法允许我们使用马尔可夫链蒙特卡洛推理来共同推断传染病的时空传播动力学。为此,我们使用非参数函数对不同位置的有效种群规模动态进行建模,从而使我们能够近似种群规模动态的范围。我们使用一系列不同的病毒爆发数据集,展示了系统地理学方法的潜在问题。然后,我们使用这些病毒数据集来激发暴发的模拟,以阐明不同系统地理学方法中存在的偏差的本质。我们表明,空间和时间动态应该联合建模,即使一个人试图恢复两者中的一个。此外,我们展示了在哪些条件下我们可以预期系统地理分析是有偏见的,特别是不同的子抽样方法,并提供了关于何时我们可以期望它们表现良好的建议。我们将MASCOT- skyline作为开源软件包MASCOT的一部分,用于贝叶斯系统动力学平台BEAST2。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions.

The spread of infectious diseases is shaped by spatial and temporal aspects, such as host population structure or changes in the transmission rate or number of infected individuals over time. These spatiotemporal dynamics are imprinted in the genomes of pathogens and can be recovered from those genomes using phylodynamics methods. However, phylodynamic methods typically quantify either the temporal or spatial transmission dynamics, which leads to unclear biases, as one can potentially not be inferred without the other. Here, we address this challenge by introducing a structured coalescent skyline approach, MASCOT-Skyline, that allows us to jointly infer spatial and temporal transmission dynamics of infectious diseases using Markov chain Monte Carlo inference. To do so, we model the effective population size dynamics in different locations using a non-parametric function, allowing us to approximate a range of population size dynamics. We show, using a range of different viral outbreak datasets, potential issues with phylogeographic methods. We then use these viral datasets to motivate simulations of outbreaks that illuminate the nature of biases present in the different phylogeographic methods. We show that spatial and temporal dynamics should be modeled jointly, even if one seeks to recover just one of the two. Further, we showcase conditions under which we can expect phylogeographic analyses to be biased, particularly different subsampling approaches, as well as provide recommendations on when we can expect them to perform well. We implemented MASCOT-Skyline as part of the open-source software package MASCOT for the Bayesian phylodynamics platform BEAST2.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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