Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Nat Rev Microbiol. 2018 Jul;16(7):410-422. doi: 10.1038/s41579-018-0029-9.
Complex microbial communities shape the dynamics of various environments, ranging from the mammalian gastrointestinal tract to the soil. Advances in DNA sequencing technologies and data analysis have provided drastic improvements in microbiome analyses, for example, in taxonomic resolution, false discovery rate control and other properties, over earlier methods. In this Review, we discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets. We focus on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis, where advances have been particularly rapid. We note that although some of these approaches are new, it is important to keep sight of the classic issues that arise during experimental design and relate to research reproducibility. We describe how keeping these issues in mind allows researchers to obtain more insight from their microbiome data sets.
复杂的微生物群落塑造了各种环境的动态，从哺乳动物的胃肠道到土壤。DNA 测序技术和数据分析的进步，在分类分辨率、错误发现率控制和其他特性方面，相较于早期方法，极大地改进了微生物组分析。在这篇综述中，我们讨论了进行微生物组研究的最佳实践，包括实验设计、分子分析技术的选择、数据分析方法以及多个组学数据集的整合。我们重点介绍了最近的发现，这些发现表明，基于操作分类单元的分析应该被基于精确序列变异的新方法所取代，还包括整合宏基因组和代谢组数据的方法，以及围绕组成数据分析的问题，在这些方面已经取得了特别迅速的进展。我们注意到，尽管其中一些方法是新的，但重要的是要看到在实验设计中出现的经典问题，以及与研究可重复性相关的问题。我们描述了如何牢记这些问题，使研究人员能够从他们的微生物组数据集获得更多的见解。
