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Best practices for analysing microbiomes

Rob Knight, Alison Vrbanac, Bryn C. Taylor, Alexander Aksenov, Chris Callewaert (UGent) , Justine Debelius, Antonio Gonzalez, Tomasz Kosciolek, Laura-Isobel McCall, Daniel McDonald, et al.
(2018) NATURE REVIEWS MICROBIOLOGY. 16(7). p.410-422
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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.
Keywords
GUT MICROBIOTA, SHOTGUN METAGENOMICS, COMMUNITY VARIATION, MASS-SPECTROMETRY, ORAL MICROBIOTA, SYSTEMS BIOLOGY, GENE-EXPRESSION, RARE BIOSPHERE, DIVERSITY, BACTERIAL

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Citation

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MLA
Knight, Rob, et al. “Best Practices for Analysing Microbiomes.” NATURE REVIEWS MICROBIOLOGY, vol. 16, no. 7, 2018, pp. 410–22, doi:10.1038/s41579-018-0029-9.
APA
Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., … Dorrestein, P. C. (2018). Best practices for analysing microbiomes. NATURE REVIEWS MICROBIOLOGY, 16(7), 410–422. https://doi.org/10.1038/s41579-018-0029-9
Chicago author-date
Knight, Rob, Alison Vrbanac, Bryn C. Taylor, Alexander Aksenov, Chris Callewaert, Justine Debelius, Antonio Gonzalez, et al. 2018. “Best Practices for Analysing Microbiomes.” NATURE REVIEWS MICROBIOLOGY 16 (7): 410–22. https://doi.org/10.1038/s41579-018-0029-9.
Chicago author-date (all authors)
Knight, Rob, Alison Vrbanac, Bryn C. Taylor, Alexander Aksenov, Chris Callewaert, Justine Debelius, Antonio Gonzalez, Tomasz Kosciolek, Laura-Isobel McCall, Daniel McDonald, Alexey V. Melnik, James T. Morton, Jose Navas, Robert A. Quinn, Jon G. Sanders, Austin D. Swafford, Luke R. Thompson, Anupriya Tripathi, Zhenjiang Z. Xu, Jesse R. Zaneveld, Qiyun Zhu, J. Gregory Caporaso, and Pieter C. Dorrestein. 2018. “Best Practices for Analysing Microbiomes.” NATURE REVIEWS MICROBIOLOGY 16 (7): 410–422. doi:10.1038/s41579-018-0029-9.
Vancouver
1.
Knight R, Vrbanac A, Taylor BC, Aksenov A, Callewaert C, Debelius J, et al. Best practices for analysing microbiomes. NATURE REVIEWS MICROBIOLOGY. 2018;16(7):410–22.
IEEE
[1]
R. Knight et al., “Best practices for analysing microbiomes,” NATURE REVIEWS MICROBIOLOGY, vol. 16, no. 7, pp. 410–422, 2018.
@article{8678762,
  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.}},
  author       = {{Knight, Rob and Vrbanac, Alison and Taylor, Bryn C. and Aksenov, Alexander and Callewaert, Chris and Debelius, Justine and Gonzalez, Antonio and Kosciolek, Tomasz and McCall, Laura-Isobel and McDonald, Daniel and Melnik, Alexey V. and Morton, James T. and Navas, Jose and Quinn, Robert A. and Sanders, Jon G. and Swafford, Austin D. and Thompson, Luke R. and Tripathi, Anupriya and Xu, Zhenjiang Z. and Zaneveld, Jesse R. and Zhu, Qiyun and Caporaso, J. Gregory and Dorrestein, Pieter C.}},
  issn         = {{1740-1526}},
  journal      = {{NATURE REVIEWS MICROBIOLOGY}},
  keywords     = {{GUT MICROBIOTA,SHOTGUN METAGENOMICS,COMMUNITY VARIATION,MASS-SPECTROMETRY,ORAL MICROBIOTA,SYSTEMS BIOLOGY,GENE-EXPRESSION,RARE BIOSPHERE,DIVERSITY,BACTERIAL}},
  language     = {{eng}},
  number       = {{7}},
  pages        = {{410--422}},
  title        = {{Best practices for analysing microbiomes}},
  url          = {{http://doi.org/10.1038/s41579-018-0029-9}},
  volume       = {{16}},
  year         = {{2018}},
}
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