在基因组数据中发现病毒的生物信息学工具和方法:系统性审查
Julia Galeeva1, Polina Kuzmichenko1, Alexander Manolov1
1Research Institute for Systems Biology and Medicine (RISBM), Department of Mathematical Biology and Bioinformatics, Moscow 117246, Russia.
Viruses
|December 31, 2025
概括
本综述探讨了用于分析元基因组数据的病毒发现工具. 机器学习方法提高了对各种病毒的检测,但解释结果和处理不完整基因组的挑战仍然存在.
科学领域:
- 生物信息学是一种生物信息学.
- 病毒学 病毒学
- 计算生物学 计算生物学
背景情况:
- 病毒元基因组数据呈指数级增长,需要先进的发现工具.
- 极端的病毒多样性,快速的进化和有限的数据库挑战了传统的方法.
- 高通量测序产生了大量的数据,需要有效的分析来识别病毒.
研究的目的:
- 从1990年到2025年,系统地审查病毒发现工具的趋势和进展.
- 专注于分析高通量测序数据的第一线工具.
- 总结当前的知识和病毒检测的未来方向.
主要方法:
- 按照PRISMA 2020指南进行系统审查.
- 使用雪球式的方法搜索PubMed数据库.
- 分析了54多项关于病毒发现方法的关键研究.
主要成果:
- 基于对齐的评估,相似性估计,HMM,k-mer和机器学习方法.
- 机器学习方法显示了对不同病毒的改进检测.
- 挑战包括解释模型决策和管理不完整的病毒基因组.
结论:
- 方法学的进步,特别是机器学习,提高了病毒的发现.
- 需要进一步开发以解决可解释性和不完整基因组挑战.
- 这一审查为未来的病毒检测工具开发提供了基础.
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