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超越基本多样性估计——用于扩增子测序数据机制解释的分析工具

Beyond Basic Diversity Estimates-Analytical Tools for Mechanistic Interpretations of Amplicon Sequencing Data.

作者信息

Trego Anna, Keating Ciara, Nzeteu Corine, Graham Alison, O'Flaherty Vincent, Ijaz Umer Zeeshan

机构信息

Microbial Ecology Laboratory, School of Biological and Chemical Sciences and the Ryan Institute, University of Galway, University Road, H91 TK33 Galway, Ireland.

Institute of Biodiversity, Animal Health & Comparative Medicine, The University of Glasgow, Oakfield Avenue, Glasgow G12 8LT, UK.

出版信息

Microorganisms. 2022 Oct 1;10(10):1961. doi: 10.3390/microorganisms10101961.

Abstract

Understanding microbial ecology through amplifying short read regions, typically 16S rRNA for prokaryotic species or 18S rRNA for eukaryotic species, remains a popular, economical choice. These methods provide relative abundances of key microbial taxa, which, depending on the experimental design, can be used to infer mechanistic ecological underpinnings. In this review, we discuss recent advancements in in situ analytical tools that have the power to elucidate ecological phenomena, unveil the metabolic potential of microbial communities, identify complex multidimensional interactions between species, and compare stability and complexity under different conditions. Additionally, we highlight methods that incorporate various modalities and additional information, which in combination with abundance data, can help us understand how microbial communities respond to change in a typical ecosystem. Whilst the field of microbial informatics continues to progress substantially, our emphasis is on popular methods that are applicable to a broad range of study designs. The application of these methods can increase our mechanistic understanding of the ongoing dynamics of complex microbial communities.

摘要

通过扩增短读区域来理解微生物生态学,对于原核生物通常是16S rRNA,对于真核生物通常是18S rRNA,仍然是一种流行且经济的选择。这些方法提供了关键微生物类群的相对丰度,根据实验设计,可用于推断生态机制的基础。在本综述中,我们讨论了原位分析工具的最新进展,这些工具能够阐明生态现象、揭示微生物群落的代谢潜力、识别物种间复杂的多维相互作用,并比较不同条件下的稳定性和复杂性。此外,我们强调了结合各种模式和额外信息的方法,这些方法与丰度数据相结合,有助于我们理解微生物群落在典型生态系统中如何应对变化。虽然微生物信息学领域继续取得重大进展,但我们重点关注适用于广泛研究设计的常用方法。这些方法的应用可以增强我们对复杂微生物群落动态变化的机制理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5c1/9609705/1bd4cad7c69b/microorganisms-10-01961-g001.jpg

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