隐藏的马尔科夫模型检测了SARS-CoV-2的重组和祖先
Nobuaki Masaki1,2, Trevor Bedford2,3
1Department of Biostatistics, University of Washington, Seattle, WA.
bioRxiv : the preprint server for biology
|November 26, 2025
概括
一个新的隐藏马尔科夫模型识别了没有用户定义参数的重组SARS-CoV-2序列. 这种方法检测到7,619个重组物,与社区病毒流行率的增加相关.
科学领域:
- 病毒学 病毒学
- 基因组学就是基因组学.
- 流行病学 流行病学
背景情况:
- 在SARS-CoV-2中同源重组可以从共同感染中创建马赛克基因组.
- 现有的检测方法通常需要用户定义的参数,这限制了它们在大规模监控中的应用.
研究的目的:
- 开发一种新的,无参数的隐藏马尔科夫模型,用于检测复合SARS-CoV-2序列.
- 为了确定检测到的重组序列的父系,并适应新的突变.
主要方法:
- 使用最大概率设计了一个隐藏的马尔科夫模型,以估计过渡和排放概率.
- 该模型应用于英格兰采样的440,307个SARS-CoV-2序列 (2020年9月至2024年3月).
主要成果:
- 该模型确定了7,619个重组序列 (占总数的1.73%).
- 在每周重组序列检测和社区SARS-CoV-2流行率之间观察到积极的关联.
- 在尖端和基因间区域经常发现重组断点.
结论:
- 开发的隐藏马尔科夫模型有效地检测到没有用户定义的参数的SARS-CoV-2重组.
- 增加的病毒流行率可能会导致更高的联合感染率和随后的再组合.
- 研究结果强调了监测病毒进化和公共卫生中的重组事件的重要性.
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