使用基于商标蛋白质家族构建的随机森林模型对巨型病毒基因组进行自动分类
Anh D Ha1, Frank O Aylward1,2
1Department of Biological Sciences, Virginia Tech, Blacksburg VA, 24061.
bioRxiv : the preprint server for biology
|November 28, 2023
概括
我们开发了TIGTOG,这是一种机器学习工具,可以使用蛋白质家族概况来分类巨型病毒 (核细胞病毒科). 这种方法在分类学上准确地分配了新的病毒基因组,有助于生态和进化研究.
科学领域:
- 病毒学 病毒学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 巨型病毒 (核细胞病毒科) 在全球分布,具有生态意义.
- 它们的遗传学多样性和复杂的基因组挑战了传统的分类学分类,特别是不完整的元基因组组装基因组 (MAGs).
研究的目的:
- 开发一种基于机器学习的方法,用于准确地对新型巨型病毒MAG进行分类.
- 为了利用蛋白质家族的含量来实现强大的病毒分类学.
主要方法:
- 开发了TIGTOG (使用商标正统组的巨型病毒的分类学信息),一个随机森林算法.
- 在1,531个核细胞病毒基因组上使用巨型病毒正统组 (GVOGs) 训练模型.
- 在一组独立的823个巨型病毒基因组上验证了模型.
主要成果:
- 实现了高的交叉验证准确性:99.6% (序列) 和97.3% (家族).
- 证明了强大的验证准确性:98.6% (序列) 和95.9% (家族).
- 分类依赖于全面的基因组签名,而不是单个标记基因.
结论:
- 蛋白家族概况为分类巨型病毒提供了快速,准确的方法.
- TIGTOG增强了大型DNA病毒的分类学分配,适用于其他病毒群.
- 这种方法有助于理解病毒多样性和生态作用.
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