从RNA病毒基因组的进化特征预测储库宿主和关节动物载体
Simon A Babayan1,2, Richard J Orton3, Daniel G Streicker4,3
1Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow G12 8QQ, Scotland, UK.
Science (New York, N.Y.)
|November 3, 2018
概括
机器学习模型从基因组序列中预测RNA病毒的动物起源和载体. 通过识别未知的病毒库,加速研究和应对新出现的传染病.
科学领域:
- 病毒学
- 基因组学
- 流行病学
- 机器学习
- 生物信息学
背景情况:
- 鉴定RNA病毒的动物起源和载体对于控制新出现的传染病至关重要,但往往是一个漫长且资源密集的过程.
- 涉及广泛的实地和实验室研究的传统方法可以延迟对新型病毒威胁的关键反应.
- 基因组测序技术的快速发展已经产生了大量的病毒序列数据.
研究的目的:
- 开发和验证能够直接从基因组序列中预测RNA病毒的动物储存和关节动物载体的机器学习模型.
- 证明这些预测模型在各种人类传染性单链RNA病毒家族中的广泛适用性.
- 减少病毒发现和实施有针对性的研究,监测和管理策略之间的时间延迟.
主要方法:
- 使用大型基因组和生态数据集来训练机器学习模型.
- 开发了分析病毒基因组序列的预测算法,以推断宿主动物和关节动物载体的身份.
- 将模型应用于广泛的单链RNA病毒,包括具有以前未知的流行病学特征的病毒.
主要成果:
- 仅使用它们的基因组序列,成功预测了众多RNA病毒的动物水库和关节动物载体.
- 确定了69种病毒的起源和载体,而此前这些信息是未知的或不清楚的.
- 证明模型能够准确预测多个人类感染家族的多种病毒的流行病学.
结论:
- 病毒基因组的机器学习分析提供了一种强大而快速的方法来识别病毒的起源和载体.
- 这些预测模型可以显著加快新出现的传染病的理解和管理.
- 利用基因组数据和机器学习对于积极的公共卫生监测和应对至关重要.
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