Best practices for analysing microbiomes

doi:10.1038/s41579-018-0029-9

Review

. 2018 Jul;16(7):410-422.

doi: 10.1038/s41579-018-0029-9.

Best practices for analysing microbiomes

Rob Knight^{1

2

3}, Alison Vrbanac⁴, Bryn C Taylor⁴, Alexander Aksenov⁵, Chris Callewaert^{6

7}, Justine Debelius⁶, Antonio Gonzalez⁶, Tomasz Kosciolek⁶, Laura-Isobel McCall⁵, Daniel McDonald⁶, Alexey V Melnik⁵, James T Morton^{6

8}, Jose Navas⁸, Robert A Quinn⁵, Jon G Sanders⁶, Austin D Swafford⁹, Luke R Thompson^{10

11}, Anupriya Tripathi¹², Zhenjiang Z Xu⁶, Jesse R Zaneveld¹³, Qiyun Zhu⁶, J Gregory Caporaso¹⁴, Pieter C Dorrestein^{9

5

6}

Affiliations

¹ Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA. robknight@ucsd.edu.
² Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA. robknight@ucsd.edu.
³ Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA. robknight@ucsd.edu.
⁴ Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA.
⁵ Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
⁶ Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
⁷ Center for Microbial Ecology and Technology, Ghent University, Ghent, Belgium.
⁸ Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
⁹ Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
¹⁰ Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, MS, USA.
¹¹ Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, National Marine Fisheries Service, La Jolla, CA, USA.
¹² Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA.
¹³ Division of Biological Sciences, School of Science Technology Engineering and Math, University of Washington Bothell, Bothell, WA, USA.
¹⁴ Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.

PMID: 29795328
DOI: 10.1038/s41579-018-0029-9

Review

Best practices for analysing microbiomes

Rob Knight et al. Nat Rev Microbiol. 2018 Jul.

. 2018 Jul;16(7):410-422.

doi: 10.1038/s41579-018-0029-9.

Authors

Affiliations

¹ Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA. robknight@ucsd.edu.
² Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA. robknight@ucsd.edu.
³ Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA. robknight@ucsd.edu.
⁴ Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA.
⁵ Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
⁶ Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
⁷ Center for Microbial Ecology and Technology, Ghent University, Ghent, Belgium.
⁸ Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
⁹ Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
¹⁰ Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, MS, USA.
¹¹ Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, National Marine Fisheries Service, La Jolla, CA, USA.
¹² Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA.
¹³ Division of Biological Sciences, School of Science Technology Engineering and Math, University of Washington Bothell, Bothell, WA, USA.
¹⁴ Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.

PMID: 29795328
DOI: 10.1038/s41579-018-0029-9

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.

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Best practices for analysing microbiomes

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Best practices for analysing microbiomes

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