JUNIPER:从规模上的下一代测序数据中重建传输事件
Ivan Specht1, Gage K Moreno1, Taylor Brock-Fisher1,2
1The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Research square
|April 8, 2025
概括
我们开发了JUNIPER,这是一个可扩展的工具,通过分析病原体遗传数据来重建疾病传播网络. 朱尼珀提高了对病原体传播的理解,并有助于在疫情期间进行有针对性的控制.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 了解病原体传播动态对于有效的疾病控制至关重要.
- 传输重建的现有工具面临着可扩展性和方法上的局限性.
研究的目的:
- 开发一个高度可扩展的工具,JUNIPER (植物遗传学和流行病学重建的联合底层网络推断),用于重建病原体传播网络.
- 将宿主内变异和不完整的抽样纳入爆发重建模型.
主要方法:
- 使用下一代测序数据开发了主机内部变异频率的统计模型.
- 集成宿主内部变异与人口水平的进化和传播模型.
- 能够同时推断族系和传输树,并实现算法并行化以实现可扩展性.
主要成果:
- 在超过16万个SARS-CoV-2基因组上验证了宿主内部变异模型.
- 证明了JUNIPER在牛H5N1 (1500多例) 和人类COVID-19 (13000多例) 大规模数据集上的实用性.
- 在加利福尼亚州量化了H5N1传染率的升高,并确定了高可信度传染事件;显示了疫苗接种在减少SARS-CoV-2传染方面的有效性.
结论:
- JUNIPER克服了现有工具的计算和方法限制.
- 为大规模的病原体传播研究提供了强大的框架.
- 通过提供对疾病爆发的关键见解,促进有针对性的控制措施.
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