wavess: An R package for simulation of adaptive within-host virus sequence evolution.

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-09-18 eCollection Date: 2025-09-01 DOI:10.1371/journal.pcbi.1013437
Narmada Sambaturu, Zena Lapp, Fernando D K Tria, Ethan Romero-Severson, Carmen Molina-París, Thomas Leitner
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引用次数: 0

Abstract

Simulating within-host virus sequence evolution allows for the investigation of factors such as the role of recombination in virus diversification and the impact of selective pressures on virus evolution. Here, we provide a new software to simulate virus within-host evolution called wavess (within-host adaptive virus evolution sequence simulator), a discrete-time individual-based model and a corresponding user-friendly R package. The underlying model simulates recombination, a latent infected cell reservoir, and three forms of selection: conserved sites fitness and replicative fitness in comparison to a reference sequence, and immune fitness including cross-reactivity imposed by a co-evolving immune response. In the R package, we also provide functions to generate model inputs from empirical data, as well as functions to analyze the simulation outputs. At user-defined time points, the software returns various counts related to the virus population(s) and a set of sampled virus sequences. We applied this model to investigate the selection pressures on HIV-1 env sequences longitudinally collected from 11 individuals. The best-fitting immune cost differed across individuals, mirroring the real-world expectation of heterogeneous immune responses among human hosts. Furthermore, the phylogenies reconstructed from these simulated sequences were similar to the phylogenies reconstructed from the real sequences for all summary statistics tested. To our knowledge, compared to other similar models, wavess has been more rigorously validated against real within-host virus sequences, and is the first to be implemented as an R package. The wavess R package can be downloaded from https://github.com/MolEvolEpid/wavess.

wavess:一个R软件包,用于模拟宿主内适应性病毒序列进化。
模拟宿主内病毒序列进化允许对诸如重组在病毒多样化中的作用和选择压力对病毒进化的影响等因素进行调查。在这里,我们提供了一个新的软件来模拟病毒在宿主内的进化,称为wavess(宿主内自适应病毒进化序列模拟器),一个离散时间的基于个体的模型和相应的用户友好的R包。基础模型模拟了重组、潜伏感染细胞库和三种形式的选择:与参考序列相比的保守位点适应性和复制适应性,以及免疫适应性,包括由共同进化的免疫反应施加的交叉反应性。在R包中,我们还提供了从经验数据生成模型输入的函数,以及分析仿真输出的函数。在用户定义的时间点,软件返回与病毒种群和一组采样病毒序列相关的各种计数。我们应用该模型研究了从11个个体纵向收集的HIV-1环境序列的选择压力。最合适的免疫成本因个体而异,反映了现实世界对人类宿主异质免疫反应的期望。此外,从模拟序列重建的系统发育与从真实序列重建的系统发育相似。据我们所知,与其他类似的模型相比,wavess已经针对真实的宿主病毒序列进行了更严格的验证,并且是第一个作为R包实现的模型。wave R包可以从https://github.com/MolEvolEpid/wavess下载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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