Soil Management and Sugar Beet Research, United States Department of Agriculture Agricultural Research Service (USDA-ARS), Fort Collins, CO, USA.
Soil and Water Conservation Research Unit, USDA-ARS, Adams, OR, USA.
Commun Biol. 2024 Jul 28;7(1):913. doi: 10.1038/s42003-024-06594-8.
The sequencing platform and workflow strongly influence microbial community analyses through potential errors at each step. Effective diagnostics and experimental controls are needed to validate data and improve reproducibility. This cross-laboratory study evaluates sources of variability and error at three main steps of a standardized amplicon sequencing workflow (DNA extraction, polymerase chain reaction [PCR], and sequencing) using Oxford Nanopore MinION to analyze agricultural soils and a simple mock community. Variability in sequence results occurs at each step in the workflow with PCR errors and differences in library size greatly influencing diversity estimates. Common bioinformatic diagnostics and the mock community are ineffective at detecting PCR abnormalities. This work outlines several diagnostic checks and techniques to account for sequencing depth and ensure accuracy and reproducibility in soil community analyses. These diagnostics and the inclusion of a reference soil can help ensure data validity and facilitate the comparison of multiple sequencing runs within and between laboratories.
测序平台和工作流程通过每个步骤中的潜在错误强烈影响微生物群落分析。需要有效的诊断和实验控制来验证数据并提高可重复性。这项跨实验室研究使用 Oxford Nanopore MinION 评估了标准化扩增子测序工作流程（DNA 提取、聚合酶链反应 [PCR] 和测序）的三个主要步骤中的变异性和误差源，以分析农业土壤和简单的模拟群落。在工作流程的每个步骤中，序列结果都会发生变化，PCR 错误和文库大小的差异极大地影响了多样性估计。常见的生物信息学诊断方法和模拟群落无法有效检测到 PCR 异常。这项工作概述了几种诊断检查和技术，以考虑测序深度，并确保土壤群落分析的准确性和重现性。这些诊断方法和参考土壤的包含可以帮助确保数据的有效性，并促进实验室内部和实验室之间的多个测序运行的比较。
