U.S. Department of Agriculture, Agricultural Research Service, U.S. National Poultry Research Center (USNPRC), Athens, GA 30605, USA.
Department of Animal Sciences, Colorado State University, Fort Collins, CO 80524, USA.
J Anim Sci. 2022 Feb 1;100(2). doi: 10.1093/jas/skab346.
Microbiome studies in animal science using 16S rRNA gene sequencing have become increasingly common in recent years as sequencing costs continue to fall and bioinformatic tools become more powerful and user-friendly. The combination of molecular biology, microbiology, microbial ecology, computer science, and bioinformatics-in addition to the traditional considerations when conducting an animal science study-makes microbiome studies sometimes intimidating due to the intersection of different fields. The objective of this review is to serve as a jumping-off point for those animal scientists less familiar with 16S rRNA gene sequencing and analyses and to bring up common issues and concerns that arise when planning an animal microbiome study from design through analysis. This review includes an overview of 16S rRNA gene sequencing, its advantages, and its limitations; experimental design considerations such as study design, sample size, sample pooling, and sample locations; wet lab considerations such as field handing, microbial cell lysis, low biomass samples, library preparation, and sequencing controls; and computational considerations such as identification of contamination, accounting for uneven sequencing depth, constructing diversity metrics, assigning taxonomy, differential abundance testing, and, finally, data availability. In addition to general considerations, we highlight some special considerations by species and sample type.
近年来,随着测序成本的持续下降和生物信息学工具变得更加强大和用户友好,使用 16S rRNA 基因测序的动物科学微生物组研究变得越来越普遍。分子生物学、微生物学、微生物生态学、计算机科学和生物信息学的结合——除了在进行动物科学研究时的传统考虑因素之外——由于不同领域的交叉,使得微生物组研究有时令人生畏。本综述的目的是为那些对 16S rRNA 基因测序和分析不太熟悉的动物科学家提供一个起点,并提出在从设计到分析规划动物微生物组研究时出现的常见问题和关注点。本综述包括 16S rRNA 基因测序概述及其优势和局限性;实验设计考虑因素,如研究设计、样本量、样本混合和样本位置;湿实验室考虑因素,如现场处理、微生物细胞裂解、低生物量样本、文库制备和测序对照;以及计算考虑因素,如识别污染、考虑不均匀测序深度、构建多样性指标、分类学分配、差异丰度检测,最后是数据可用性。除了一般考虑因素外,我们还按物种和样本类型强调了一些特殊考虑因素。