ASV与OTU集群:对微生物组元编码研究中的α,β和gamma多样性的影响
Andrea Fasolo1, Saptarathi Deb1, Piergiorgio Stevanato1
1Department of Agronomy, Animals, Food, Natural Resources and Environment DAFNAE, University of Padova, Padua, Italy.
PloS one
|October 3, 2024
概括
扩展序列变异 (ASV) 分析在微生物生态学研究中比传统的操作分类单位 (OTU) 集群提供了显著的优势. 与OTU方法相比,ASV分析提供了更准确的生态指标,并避免低估了与OTU方法相比的多样性.
科学领域:
- 微生物生态学和生物信息学
- 分子生态学和生物多样性评估
背景情况:
- 传统的微生物社区分析依赖于操作分类学单位 (OTU) 聚类,通常在97%的序列身份.
- 生物信息学的进步使得安普利康序列变异 (ASV) 分析成为可能,该分析能够将序列解决到单核酸差异,从而最大限度地减少技术错误.
研究的目的:
- 为了比较OTU聚类 (在97%和99%的身份) 与ASV分析对生态指标的影响.
- 为了评估这些方法的性能,使用来自多样化的环境截面的16S rRNA基因扩增子数据集.
主要方法:
- 分析了17个邻近的息地沿着700米的横断线进行的16S rRNA基因元编码数据集.
- 两个OTU集群水平 (97%和99%) 和ASV分析被应用到相同的数据集.
- 为了比较,进行了α,β和gamma多样性,主导性,均性和多变量排序分析.
主要成果:
- OTU集群对物种多样性的生态指标值大幅低估.
- 与ASV分析相比,OTU方法扭曲了主导性和均性指数行为.
- 多变量排序分析显示对集群方法的敏感性,影响树拓和连贯性.
结论:
- 以参考为基础的OTU集群引入了偏见,可能缺少新型种类和误解社区结构.
- ASV分析为 prokaryotic 社区研究提供了显著的优势,提供了更准确的生态洞察力.
- 建议在环境研究中使用ASV分析,而不是OTU集群,特别是那些涉及未表征物种的环境研究.
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