Best practices for analysing microbiomes.

1. Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
Authors
Knight R^{1,

4,

5}
Swafford AD¹
Dorrestein PC^{1,

3,

4}
(3 authors)
2. Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA.
Authors
Vrbanac A²
Taylor BC²
(2 authors)
3. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
Authors
Aksenov A³
McCall LI³
Melnik AV³
Quinn RA³
Dorrestein PC^{1,

3,

4}
(5 authors)
4. Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Authors
Knight R^{1,

4,

5}
Callewaert C⁴
Debelius J⁴
Gonzalez A⁴
Kosciolek T⁴
McDonald D⁴
Morton JT^{4,

5}
Sanders JG⁴
Xu ZZ⁴
Zhu Q⁴
Dorrestein PC^{1,

3,

4}
(11 authors)
5. Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
Authors
Knight R^{1,

4,

5}
Morton JT^{4,

5}
Navas J⁵
(3 authors)

Show all (9)

ORCIDs linked to this article

Show all (11)

Nature reviews. Microbiology, 01 Jul 2018, 16(7):410-422
https://doi.org/10.1038/s41579-018-0029-9 PMID: 29795328

Review

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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https://en.wikipedia.org/wiki/Human_microbiome

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Best practices for analysing microbiomes.

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Abstract

Full text links

References

Skin Microbiome Surveys Are Strongly Influenced by Experimental Design.

Population-level analysis of gut microbiome variation.

Gut Microbiota Linked to Sexual Preference and HIV Infection.

Linking long-term dietary patterns with gut microbial enterotypes.

Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota.

Proton pump inhibitors alter the composition of the gut microbiota.

Dynamics of the human gut microbiome in inflammatory bowel disease.

Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA.

Tiny microbes, enormous impacts: what matters in gut microbiome studies?

La Rosa, P. S. et al. Hypothesis testing and power calculations for taxonomic-based human microbiome data. PLoS ONE. 7, e52078 (2012).

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Smart citations by scite.ai
Explore citation contexts and check if this article has been supported or disputed.
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Article citations

Evaluation of inter- and intra-variability in gut health markers in healthy adults using an optimised faecal sampling and processing method.

A metagenomic approach to demystify the anaerobic digestion black box and achieve higher biogas yield: a review.

Emergent ecological patterns and modelling of gut microbiomes in health and in disease.

Standardization of gut microbiome analysis in sports.

Effects of Fertilization and Planting Modes on Soil Organic Carbon and Microbial Community Formation of Tree Seedlings.

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Wikipedia

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Best practices for analysing microbiomes.

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La Rosa, P. S. et al. Hypothesis testing and power calculations for taxonomic-based human microbiome data. PLoS ONE. 7, e52078 (2012).

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