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利用机器学习来对新出现的天体病毒进行分类

Fatemeh Alipour¹, Connor Holmes², Yang Young Lu¹

¹School of Computer Science, University of Waterloo, Waterloo, ON, Canada.

Frontiers in molecular biosciences

|January 26, 2024

概括

一种新的机器学习方法使用全基因组序列和宿主数据对天体病毒进行分类,准确标记了191个未分类的病毒基因组,并确定了潜在的新子类.

科学领域:

病毒学病毒学
生物信息学是一种生物信息学.
机器学习机器学习

背景情况:

天体病毒导致人类和鸟类的重大疾病,给健康和经济带来挑战.
目前以宿主为基础的分类与观察到的跨物种传播和新病毒发现作斗争.
下一代测序产生了大量的病毒基因组数据,需要先进的分类工具.

研究的目的:

为未经分类的天体病毒开发一种新,强大的分类学分类方法.
利用全基因组序列组成和宿主信息进行准确的病毒分类.
为了解决遗传重组对病毒分类学的影响.

主要方法:

一种三角式方法,将监督和无监督的机器学习与宿主物种数据相结合.
全基因组序列k-mer组合分析以确定病毒关系.
包括一个组件来检测和考虑重组序列.

主要成果:

为此前191个未经分类的天体病毒基因组提出了属标签.
确定了8个具有宿主物种不相容的额外的天体病毒基因组,表明跨物种传播.
通过使用机器学习和PCA.提供了人类天体病毒 (HAstV) 和天体病毒 (GoAstV) 分类的证据.

结论:

拟议的机器学习方法为天体病毒的分类学分类提供了一个快速,可靠和可扩展的方法.
这种方法可以有效地分类新出现的病毒,并跟上高通量测序数据的步伐.
这些发现支持对天体病毒分类学进行重新评估,并识别新的病毒亚属.

关键词:

亚瓦斯特罗病毒病毒.马马斯特病毒病毒.没有对齐的分类分类.星座病毒家族 (Astroviridae) 的一个家族.基因组签名基因组签名在 k-mer 频率上.机器学习是机器学习.病毒分类和集群是病毒的分类和集群.

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