Optimizing methods and dodging pitfalls in microbiome research

• Published in: Microbiome Vol. 5; no. 1; pp. 52 - 14
• Main Authors: Kim, Dorothy, Hofstaedter, Casey E, Zhao, Chunyu, Mattei, Lisa, Tanes, Ceylan, Clarke, Erik, Lauder, Abigail, Sherrill-Mix, Scott, Chehoud, Christel, Kelsen, Judith, Conrad, Máire, Collman, Ronald G, Baldassano, Robert, Bushman, Frederic D, Bittinger, Kyle
• Format: Journal Article
• Language: English
• Published: England BioMed Central 05.05.2017; BMC
• Subjects: 16S rRNA gene; Age; Animals; Antibiotics; Biomass; Contamination; Deoxyribonucleic acid; Diabetes; Diet; DNA; Environmental contamination; Experiments; Gender differences; Gene expression; Genomes; Humans; Influence; Metagenomics; Methods; Microbiological Techniques - methods; Microbiological Techniques - standards; Microbiota; Nucleotide sequence; Pediatrics; Prescription drugs; Reproducibility of Results; Research Design - standards; Review; Sexual orientation; Shotgun metagenomics; Studies; Study design; Substance abuse treatment; Study design; Metagenomics; Environmental contamination; Best practices; Methods; 16S rRNA gene; Shotgun metagenomics
• ISSN: 2049-2618; 2049-2618
• DOI: 10.1186/s40168-017-0267-5

Research on the human microbiome has yielded numerous insights into health and disease, but also has resulted in a wealth of experimental artifacts. Here, we present suggestions for optimizing experimental design and avoiding known pitfalls, organized in the typical order in which studies are carried out. We first review best practices in experimental design and introduce common confounders such as age, diet, antibiotic use, pet ownership, longitudinal instability, and microbial sharing during cohousing in animal studies. Typically, samples will need to be stored, so we provide data on best practices for several sample types. We then discuss design and analysis of positive and negative controls, which should always be run with experimental samples. We introduce a convenient set of non-biological DNA sequences that can be useful as positive controls for high-volume analysis. Careful analysis of negative and positive controls is particularly important in studies of samples with low microbial biomass, where contamination can comprise most or all of a sample. Lastly, we summarize approaches to enhancing experimental robustness by careful control of multiple comparisons and to comparing discovery and validation cohorts. We hope the experimental tactics summarized here will help researchers in this exciting field advance their studies efficiently while avoiding errors.