整合多omics数据以阐明微生物形成生态系统中的关键石未知的种类
Rocío Amorín de Hegedüs1,2, Ana Conesa3, Jamie S Foster2
1Genetics Institute, University of Florida, Gainesville, FL, United States.
Frontiers in microbiology
|August 14, 2023
概括
大多数微生物物种仍然未知,阻碍了对生态系统的理解. 这项研究整合了多种测序数据类型,以分析微生物群落中的"微生物暗物质",揭示了它在生态系统结构中的关键作用.
科学领域:
- 微生物学 微生物学
- 生态系统科学 生态系统科学
- 生物信息学是一种生物信息学.
背景情况:
- 微生物显著影响地球的生态系统,但大多数物种尚未确定,造成知识差距.
- 了解这些未知的种类,称为"微生物暗物质",对于理解复杂的生态系统至关重要.
研究的目的:
- 评估微生物形成社区中"微生物暗物质"的丰度和社区结构.
- 为了比较不同的高通量测序数据类型 (amplicon,metagenomic,metatranscriptomic) 用于分析微生物暗物质.
- 确定关键的微生物种类及其在生态系统中的连接性.
主要方法:
- 整合了amplicon,元基因组和元转录组测序数据.
- 使用SortMeRNA提取16SrRNA基因从元基因组和元转录组数据中读取.
- 使用QIIME2处理序列进行分类学分析,使用mdmnets进行并发网络构建.
主要成果:
- 微生物种群的很大一部分,即使在类层面,在分析的数据集中仍然未被分类.
- 同时发生的网络分析显示,许多中央网络枢纽包括未知的微生物种群.
- 通过对不同数据类型的网络枢纽进行比较,确定了对微生物形成社区结构至关重要的关键分类.
结论:
- 未知的微生物种类在构建微生物群落中发挥着重要作用.
- 使用集成的多omics数据构建生态系统网络是一种可行的方法,用于识别关键类型.
- 这种方法可以帮助揭示生态系统中以前未知的微生物的功能.
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