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Best practices for analysing microbiomes

Nature Reviews Microbiology volume 16, pages 410–422 (2018)Cite this article

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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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Fig. 1: Experimental design considerations for microbiome experiments.
Fig. 2: Best workflow for 16S ribosomal RNA, metagenomic and metatranscriptomic sequencing.
Fig. 3: Integrating omics data with microbiome data.

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Acknowledgements

This review is informed by our work funded by the National Institutes of Health, National Science Foundation, Alfred P. Sloan Foundation, John Templeton Foundation and W. M. Keck Foundation, as well as that of hundreds of collaborators on the Human Microbiome Project, American Gut Project and Earth Microbiome Project.

Reviewer information

Nature Reviews Microbiology thanks J. Raes and other anonymous reviewers for their contributions to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Rob Knight, Alison Vrbanac, Bryn C. Taylor

Authors and Affiliations

  1. Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA

    Rob Knight, Austin D. Swafford & Pieter C. Dorrestein

  2. Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA

    Alison Vrbanac & Bryn C. Taylor

  3. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA

    Alexander Aksenov, Laura-Isobel McCall, Alexey V. Melnik, Robert A. Quinn & Pieter C. Dorrestein

  4. Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA

    Rob Knight, Chris Callewaert, Justine Debelius, Antonio Gonzalez, Tomasz Kosciolek, Daniel McDonald, James T. Morton, Jon G. Sanders, Zhenjiang Z. Xu, Qiyun Zhu & Pieter C. Dorrestein

  5. Center for Microbial Ecology and Technology, Ghent University, Ghent, Belgium

    Chris Callewaert

  6. Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA

    Rob Knight, James T. Morton & Jose Navas

  7. Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, MS, USA

    Luke R. Thompson

  8. Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, National Marine Fisheries Service, La Jolla, CA, USA

    Luke R. Thompson

  9. Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA

    Anupriya Tripathi

  10. Division of Biological Sciences, School of Science Technology Engineering and Math, University of Washington Bothell, Bothell, WA, USA

    Jesse R. Zaneveld

  11. Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA

    J. Gregory Caporaso

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Contributions

A.V. and B.C.T. researched the data for the article. A.G., T.K., D.M., J.N., J.G.S. and J.R.Z. substantially contributed to discussion of content. R.K., A.V., B.C.T., A.A., C.C., J.D., L.M., A.V.M., J.T.M., R.A.Q., L.R.T., A.T., Z.Z.X., Q.Z. and J.G.C. wrote the article. R.K., A.V., B.C.T., T.K., D.M., A.D.S. and P.C.D. reviewed and edited the manuscript before submission.

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Correspondence to Rob Knight.

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Glossary

Exact sequence variants

For marker gene sequencing, the exact DNA sequence for each read is used instead of operational taxonomic unit clustering.

Operational taxonomic units

(OTUs). A group of closely related individuals or sequences (often 97% sequence similarity threshold).

Machine learning

The use of algorithms to learn from and make predictions about data.

Metadata

Information about the data. In many studies, this is structured as a matrix with samples as rows and metadata categories (age, sex, longitude, season, disease state, average monthly rainfall, and so on) as columns.

Alpha diversity

A measure of within-sample diversity.

Effect size analysis

Quantification of the magnitude of an effect of a particular metadata category (treatment group, sex and sequencing plate) on the data.

Marker genes

Conserved genes (commonly 16S ribosomal RNA (rRNA), internal transcribed spacer (ITS) and 18 S rRNA) that typically contain a highly variable region that can be used for detailed identification that is flanked by highly conserved regions that can serve as binding sites for PCR primers.

Nested statistical tests

Statistical tests that address variables related to the main effect. For example, soil plot would be a nested factor for testing the effects of a fertilizer on the soil microbiota.

Coprophagic

Involving the consumption of faeces. Many animal species eat faeces to more efficiently break down plant matter by digesting the material twice.

Reads

Inferred sequences of base pairs in a single DNA fragment.

Metatranscriptome

The total content of gene transcripts from a community of organisms.

Humic substances

Produced by biodegrading organic matter; humic substances are the main component of humus (soil).

Metagenomes

The collection of genetic material from a community of organisms, for example, the genetic material from all microorganisms in the human gut microbiome.

Naive Bayesian classifier

A simple probabilistic classifier used in machine learning that is based on applying Bayes’ theorem assuming strong independence between the features.

K-mers

All possible sequences of length k from a read obtained through DNA sequencing.

Beta diversity

A measure of similarity between samples.

Faith’s phylogenetic diversity

An alpha diversity metric that uses a phylogenetic tree to compute sample diversity.

Shannon index

A commonly used index to characterize species diversity in a community.

False discovery rates

A method of understanding the rate of type I errors in null hypothesis testing when performing multiple comparisons.

Isometric log ratio transform

(ilr). Converts a vector of proportions into a vector of log ratios using a tree as a reference. The computed log ratios consist of the difference of mean logarithms of species proportions between adjacent clades within the tree.

Random forests regression

A machine learning technique that uses decision trees to perform classification.

Family-wise error

The probability of making one or more type I errors (false discoveries) when performing multiple hypotheses tests.

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Knight, R., Vrbanac, A., Taylor, B.C. et al. Best practices for analysing microbiomes. Nat Rev Microbiol 16, 410–422 (2018). https://doi.org/10.1038/s41579-018-0029-9

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