Best practices for analysing microbiomes.
Knight R, Vrbanac A, Taylor BC, Aksenov A, Callewaert C, Debelius J, Gonzalez A, Kosciolek T, McCall LI, McDonald D, Melnik AV, Morton JT, Navas J, Quinn RA, Sanders JG, Swafford AD, Thompson LR, Tripathi A, Xu ZZ, Zaneveld JR, Zhu Q, Caporaso JG, Dorrestein PC
Nat Rev Microbiol. Jul 2018. doi: 10.1038/s41579-018-0029-9
COMMENT: In this review Knight and coworkers discuss how all stages of conducting a microbiome study, from designing the experiment to collecting and storing the samples to obtaining insight from graphical displays of the sequence data, can substantially impact the results and their biological interpretation. As the effects of many of these technical steps are large compared with the real biological variability to be explained, standardization is necessary in order to compare and combine separate studies.
The authors 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. They 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. They 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.
Finally they describe how keeping these issues in mind allows researchers to obtain more insight from their microbiome data sets and conclude:
Increased standardization of techniques and dissemination of methods with low noise and bias will greatly increase the ability of the microbiome field to deliver on the promise of translatability from lab-scale studies to the clinic, field or natural environment.