Best Practices for Analysing Microbiomes

Authors

Rob Knight, University of California, San DiegoFollow
Alison Vrbanac, University of California, San Diego
Bryn C. Taylor, University of California, San Diego
Alexander Aksenov, University of California, San Diego
Chris Callewaert, University of California, San Diego
Justine Debelius, University of California, San Diego
Antonio Gonzalez, University of California, San DiegoFollow
Tomasz Kosciolek, University of California, San DiegoFollow
Laura-Isobel McCall, University of California, San Diego
Daniel McDonald, University of California, San DiegoFollow
Alexey V. Melnik, University of California, San Diego
James T. Morton, University of California, San DiegoFollow
Jose Navas, University of California, San Diego
Robert A. Quinn, University of California, San Diego
Jon G. Sanders, University of California, San DiegoFollow
Austin D. Swafford, University of California, San DiegoFollow
Luke R. Thompson, University of Southern MississippiFollow
Anupriya Tripathi, University of California, San DiegoFollow
Zhenjiang Z. Xu, University of California, San DiegoFollow
Jesse R. Zaneveld, University of Washington, Bothell
Qiyun Zhu, University of California, San DiegoFollow
J. Gregory Caporaso, Northern Arizona University
Pieter C. Dorrestein, University of California, San DiegoFollow

Document Type

Article

Publication Date

5-23-2018

Department

Coastal Sciences, Gulf Coast Research Laboratory

School

Ocean Science and Engineering

Abstract

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.

Publication Title

Nature Reviews Microbiology

Volume

16

First Page

410

Last Page

422

Recommended Citation

Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L., McDonald, D., Melnik, A. V., Morton, J. T., Navas, J., Quinn, R. A., Sanders, J. G., Swafford, A. D., Thompson, L. R., Tripathi, A., Xu, Z. Z., Zaneveld, J. R., Zhu, Q., Caporaso, J., Dorrestein, P. C. (2018). Best Practices for Analysing Microbiomes. Nature Reviews Microbiology, 16, 410-422.
Available at: https://aquila.usm.edu/fac_pubs/16614