Pitfalls and pointers: An accessible guide to marker gene amplicon sequencing in ecological applications
Pitfalls and pointers: An accessible guide to marker gene amplicon sequencing in ecological applications
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DOI：
10.1111/2041-210x.13764
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发表时间：
2021-11
影响因子：
6.6
通讯作者：
Anita Porath‐Krause;Alexander T. Strauss;Jeremiah A. Henning;E. Seabloom;E. Borer
中科院分区：
文献类型：
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作者：
Anita Porath‐Krause;Alexander T. Strauss;Jeremiah A. Henning;E. Seabloom;E. Borer
下一代测序（NGS）是一种强大的工具，已被许多研究微生物群落的生态学家迅速采用。尽管NGS技术作为生态研究工具的展示令人兴奋，但其使用固有的神秘陷阱可能会掩盖NGS数据的正确解释。在这里，我们提供了使用标记基因扩增子序列 (MGAS) 的 NGS 过程的易于理解的概述，该过程将使科学家，特别是群落生态学家能够做出适当的方法选择，并了解从 MGAS 数据中推断群落组成和多样性的限制。我们描述了 MGAS 管道，特别关注生态文献中较少强调的神秘变异来源，但它可能会极大地影响关于微生物群落多样性和组成的推断。通过根据已发表的微生物组数据模拟群落，我们演示了这些变异来源如何产生不准确或误导性的模式。我们特别强调了研究人员没有意识到的样品稀释和泳道间的变异性，这是 MGAS 管道中出现的两个神秘的变异源。这些变异来源影响对物种存在和相对丰度的估计，特别是对于中度至低丰度的物种。这些偏差来源中的每一个都可能导致对微生物群落内的绝对丰度和相对丰度以及微生物群落之间的周转的估计错误。认识和理解在 MGAS 生成过程中发生的事情，特别是为什么会发生，是生成强大的数据集和构建强大的社区矩阵的关键。向测序中心请求样本稀释信息，包括跨测序泳道的技术重复，并了解采样强度和群落分类单元分布模式如何影响群落丰富度、均匀度和多样性的测量，对于使用 MGAS 数据得出正确的生态推论至关重要。
Next‐Generation Sequencing (NGS) is a powerful tool that has been rapidly adopted by many ecologists studying microbial communities. Despite the exciting demonstration of NGS technology as a tool for ecological research, cryptic pitfalls inherent to its use can obscure correct interpretation of NGS data. Here, we provide an accessible overview of a NGS process that uses marker gene amplicon sequences (MGAS) that will allow scientists, particularly community ecologists, to make appropriate methodological choices and understand limits on inference about community composition and diversity that can be drawn from MGAS data. We describe the MGAS pipeline, focusing specifically on cryptic sources of variation that have received less emphasis in the ecological literature, but which may substantially impact inference about microbial community diversity and composition. By simulating communities from published microbiome data, we demonstrate how these sources of variation can generate inaccurate or misleading patterns. We specifically highlight sample dilution without researcher awareness and lane‐to‐lane variability, two cryptic sources of variation arising during the MGAS pipeline. These sources of variation affect estimates of species presence and relative abundance, particularly for species with moderate to low abundances. Each of these sources of bias can lead to errors in the estimation of both absolute and relative abundance within, and turnover among, microbial communities. Awareness and understanding of what happens and, specifically, why it happens during MGAS generation is key to generating a strong dataset and building a robust community matrix. Requesting sample dilution information from the sequencing centre, including technical replicates across sequencing lanes, and understanding how sampling intensity and community taxa distribution patterns shape the measurement of community richness, evenness and diversity are critical for drawing correct ecological inferences using MGAS data.