Considerations and best practices in animal science 16S ribosomal RNA gene sequencing microbiome studies. (1st February 2022)

• Record Type: Journal Article
• Authors: Weinroth, Margaret D; Belk, Aeriel D; Dean, Chris; Noyes, Noelle; Dittoe, Dana K; Rothrock, Michael J; Ricke, Steven C; Myer, Phillip R; Henniger, Madison T; Ramírez, Gustavo A; Oakley, Brian B; Summers, Katie Lynn; Miles, Asha M; Ault-Seay, Taylor B; Yu, Zhongtang; Metcalf, Jessica L; Wells, James E
• Is Part Of: Journal of animal science. Volume 100:Number 2(2022)
• Publication Date: 2022-02-01
• Subjects: 16S rRNA gene -- amplicon sequencing -- bacteriome -- bioinformatics -- microbiome; Livestock -- Periodicals; Livestock; Electronic journals; Periodicals; 636.005
• DOI: 10.1093/jas/skab346
• Languages: English
• ISSNs: 0021-8812

Abstract

Abstract: 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.