Best practices for analysing microbiomes
Open accessAbstract
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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Recent efforts have been made to consolidate and standardize sequencing and analysis protocols to improve overall quality and compatibility of microbiome research.
“Recent efforts have been made to consolidate and standardize sequencing and analysis protocols, to improve overall quality and compatibility of research in the microbiome field (Knight et al., 2018;Mallick et al., 2017;Pollock et al., 2018).”
From The Microbiota-Gut-Brain Axis (2019) · cites this paper as Knight et al., 2018
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Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · see citing works
A human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624 · see citing works
Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
Langille, M. G. I., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., Reyes, J. A., Clemente, J. C., Burkepile, D. E., Vega Thurber, R. L., Knight, R., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2676 · Cited by 9,116 · see citing works
Self-citationO'Leary, N. A., Wright, M. W., Brister, J. R., Ciufo, S., Haddad, D., McVeigh, R., Rajput, B., Robbertse, B., Smith-White, B., Ako-Adjei, D., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1189 · Cited by 6,357 · see citing works
Kraken: ultrafast metagenomic sequence classification using exact alignments
Wood, D. E., & Salzberg, S. L. (2014) · Genome Biology · doi:10.1186/gb-2014-15-3-r46 · Cited by 4,521 · see citing works
Bacterial Community Variation in Human Body Habitats Across Space and Time
Costello, E. K., Lauber, C. L., Hamady, M., Fierer, N., Gordon, J. I., & Knight, R. (2009) · Science · doi:10.1126/science.1177486 · Cited by 3,231 · see citing works
Self-citationPopulation-level analysis of gut microbiome variation
Falony, G., Joossens, M., Vieira-Silva, S., Wang, J., Darzi, Y., Faust, K., Kurilshikov, A., Bonder, M. J., Valles-Colomer, M., Vandeputte, D., et al. (2016) · Science · doi:10.1126/science.aad3503 · Cited by 2,400 · see citing works
Shotgun metagenomics, from sampling to analysis
Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata, N. (2017) · Nature Biotechnology · doi:10.1038/nbt.3935 · Cited by 2,202 · see citing works
MetaPhlAn2 for enhanced metagenomic taxonomic profiling
Truong, D. T., Franzosa, E. A., Tickle, T. L., Scholz, M., Weingart, G., Pasolli, E., Tett, A., Huttenhower, C., & Segata, N. (2015) · Nature Methods · doi:10.1038/nmeth.3589 · Cited by 2,185 · see citing works
Analysis of composition of microbiomes: a novel method for studying microbial composition
Mandal, S., Van Treuren, W., White, R. A., Eggesbø, M., Knight, R., & Peddada, S. D. (2015) · Microbial Ecology in Health & Disease · doi:10.3402/mehd.v26.27663 · Cited by 1,942 · see citing works
Self-citationDeblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns
Amir, A., McDonald, D., Navas-Molina, J. A., Kopylova, E., Morton, J. T., Zech Xu, Z., Kightley, E. P., Thompson, L. R., Hyde, E. R., Gonzalez, A., et al. (2017) · mSystems · doi:10.1128/msystems.00191-16 · Cited by 1,881 · see citing works
Self-citationPersistent gut microbiota immaturity in malnourished Bangladeshi children
Subramanian, S., Huq, S., Yatsunenko, T., Haque, R., Mahfuz, M., Alam, M. A., Benezra, A., DeStefano, J., Meier, M. F., Muegge, B. D., et al. (2014) · Nature · doi:10.1038/nature13421 · Cited by 1,270 · see citing works
Theriot, C. M., Koenigsknecht, M. J., Carlson, P. E., Hatton, G. E., Nelson, A. M., Li, B., Huffnagle, G. B., Z. Li, J., & Young, V. B. (2014) · Nature Communications · doi:10.1038/ncomms4114 · Cited by 974 · see citing works
Xenobiotics shape the physiology and gene expression of the active human gut microbiome
Maurice, C. F., Haiser, H. J., & Turnbaugh, P. J. (2013) · Cell · doi:10.1016/j.cell.2012.10.052 · Cited by 811 · see citing works
The Microbiome Quality Control Project Consortium, Sinha, R., Abu-Ali, G., Vogtmann, E., Fodor, A. A., Ren, B., Amir, A., Schwager, E., Crabtree, J., Ma, S., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3981 · Cited by 587 · see citing works
Self-citation(2014) · Cell Host & Microbe · doi:10.1016/j.chom.2014.08.014 · Cited by 536 · see citing works
Evaluation of 16S rDNA-Based Community Profiling for Human Microbiome Research
Jumpstart Consortium Human Microbiome Project Data Generation Working Group (2012) · PLoS ONE · doi:10.1371/journal.pone.0039315 · Cited by 297 · see citing works
Bikel, S., Valdez-Lara, A., Cornejo-Granados, F., Rico, K., Canizales-Quinteros, S., Soberón, X., Del Pozo-Yauner, L., & Ochoa-Leyva, A. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2015.06.001 · Cited by 263 · see citing works
Vollmers, J., Wiegand, S., & Kaster, A.-K. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169662 · Cited by 240 · see citing works
Site and Strain-Specific Variation in Gut Microbiota Profiles and Metabolism in Experimental Mice
Friswell, M. K., Gika, H., Stratford, I. J., Theodoridis, G., Telfer, B., Wilson, I. D., & McBain, A. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0008584 · Cited by 207 · see citing works
Flagged first, then by citations
Research works printed but not linked: 31
- Meisel J.S., Hannigan G.D., Tyldsley A.S., et al. Skin microbiome surveys are strongly influenced by experimental design. The Journal of investigative dermatology. 136(5):947- 956. (2016).
- Halfvarson, J. Dynamics of the human gut microbiome in inflammatory bowel disease. Nat. Microbiol. 2, 17004 (2017).
- La Rosa PS, Brooks JP, Deych E, et al. Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data. White EP, ed. PLoS ONE. 7(12):e52078 (2012).
- Dethlefsen, L. & Relman, D. A. Incomplete Recovery and Individualized Responses of the Human Distal Gut Microbiota to Repeated Antibiotic Perturbation. Proc. Natl Acad. Sci. USA 108, 4554-4561 (2011).
- Fierer, N., Hamady, M., Lauber, C. L., & Knight, R. The influence of sex, handedness, and washing on the diversity of hand surface bacteria. Proc. Natl Acad. Sci. USA 105, 17994- 17999 (2008).
- McDonald, D., Birmingham, A., & Knight, R. Context and the human microbiome. Microbiome. 3, 52 (2015).
- Ridaura, V. K. et al. Cultured Gut Microbiota from Twins Discordant for Obesity Modulate Adiposity and Metabolic Phenotypes in Mice. Science. 341, 6150 (2013).
- Reber, S. O. et al. Immunization with a Heat-Killed Preparation of the Environmental Bacterium Mycobacterium Vaccae Promotes Stress Resilience in Mice. Proc. Natl Acad. Sci. USA 113, E3130-E3139 (2016).
- Ley, R. E., Bäckhed, F., Turnbaugh, P., Lozupone, C. A., Knight, R. D., & Gordon, J. I. Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA 102, 11070-11075 (2005).
- Snijders, A. M., Langley, S. A., Kim, Y., et al. Influence of early life exposure, host genetics and diet on the mouse gut microbiome and metabolome. Nature Microbiology. 2, 16221 (2016).
- Salter, S. J. et al. Reagent and Laboratory Contamination Can Critically Impact Sequence- Based Microbiome Analyses. BMC Biol. 12, 87 (2014).
- Fouhy, F. et al. The Effects of Freezing on Faecal Microbiota as Determined Using MiSeq Sequencing and Culture-Based Investigations. PLoS One. 10, e0119355 (2015).
- Walker, A. W., Martin, J. C., Scott, P., Parkhill, J., Flint, H. J., & Scott, K. P. 16S rRNA gene-based profiling of the human infant gut microbiota is strongly influenced by sample processing and PCR primer choice. Microbiome. 3, 26 (2015).
- Okuda, S., Tsuchiya, Y., Kiriyama, C., Itoh, M. & Morisaki, H. Virtual metagenome reconstruction from 16S rRNA gene sequences. Nat. Commun. 3, 1203 (2012).
- Jun, S. R., Robeson, M. S., Hauser, L. J., Schadt, C. W., & Gorin, A. A. PanFP: pangenome- based functional profiles for microbial communities. BMC Res. Notes. 8, 479 (2015).
- Abubucker, Sahar et al. Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome. PLoS Comput. Biol. 8, e1002358 (2012).
- Carini, P., Marsden, P. J., Leff, J. W., Morgan, E. E., Strickland, M. S. & Fierer, N. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat Microbiol. 2, 16242 (2016).
- Giannoukos, G. et al. Efficient and Robust RNA-Seq Process for Cultured Bacteria and Complex Community Transcriptomes. Genome Biol. 13, 3 (2012).
- Wang Y, Hayatsu M, Fujii T. Extraction of Bacterial RNA from Soil: Challenges and Solutions. Microbes and Environments. 27(2):111-121 (2012).
- Tveit AT, Urich T, Svenning MM. Metatranscriptomic Analysis of Arctic Peat Soil Microbiota. Voordouw G, ed. Applied and Environmental Microbiology. 80(18):5761-5772 (2014).
- Franzosa, E. A. et al. Relating the Metatranscriptome and Metagenome of the Human Gut. Proc. Natl Acad. Sci. USA 111, E2329-E2338 (2014).
- McIntyre, A. B. R. et al. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol. 18, 182 (2017).
- Huson, D. H. et al. MEGAN Community Edition -Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data. PLoS Comput. Biol. 12, (2016).
- Prestat, E., et al. FOAM (Functional Ontology Assignments for Metagenomes): a Hidden Markov Model (HMM) database with environmental focus. Nucleic Acids Res. 42, e145 (2014).
- Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science. 348, 6237 (2015).
- Howe, A. C., Jansson, J. K., Malfatti, S. A., Tringe, S. G., Tiedje, J. M., & Brown, C. T. Tackling soil diversity with the assembly of large, complex metagenomes. Proc. Natl. Acad. Sci. USA. 111, 4904-4909 (2014).
- Narayanasamy, S. et al. IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses. Genome Biol. 17, 260 (2016).
- Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).
- Laczny, C. C. et al. VizBin -an application for reference-independent visualization and human-augmented binning of metagenomic data. Microbiome. 3, 1 (2015).
- Eren, A. M. et al. Anvi'o: an advanced analysis and visualization platform for 'omics data. PeerJ. 3, e1319 (2015). 100. White Iii, R. A. et al. ATLAS (Automatic Tool for Local Assembly Structures) -a comprehensive infrastructure for assembly, annotation, and genomic binning of metagenomic and metatranscriptomic data. PeerJ. Prepr. 1-11 (2017). 101. Treangen, T. J. et al. MetAMOS: a modular and open source metagenomic assembly and analysis pipeline. Genome Biol. 14, R2 (2013). 102. Robinson, M. D., McCarthy, D. J., & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 26, 139- 140 (2010). 103. Anders, S., & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010). 104. Sczyrba, A. et al. Critical Assessment of Metagenome Interpretation -a benchmark of computational metagenomics software. bioRxiv. 99127 (2017). 105. Barwell, L. J., Isaac, N. J. B., & Kunin, W. E. Measuring β-diversity with species abundance data. J. Anim. Ecol. 84, 1112-1122 (2015). 106. Kuczynski, J., et al. Microbial community resemblance methods differ in their ability to detect biologically relevant patterns. Nat. Methods. 7, 813-819 (2010). 107. Hamady, M., Lozupone, C. & Knight, R. Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J. 4, 17-27 (2010). 108. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927- 930 (2003) 109.
- Morton, J. T. et al. Balance Trees Reveal Microbial Niche Differentiation. mSystems. 2, e00162-16 (2017). 123. Vandeputte, D., Kathagen, G., D'hoe, K. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature. 551, 507-511 (2017). 124. Kleyer, H., Tecon. R., Or, D. Resolving Species Level Changes in a Representative Soil Bacterial Community Using Microfluidic Quantitative PCR. Front Microbiol. 8:2017 (2017). 125. Knights, D., Parfrey, L. W., Zaneveld, J., Lozupone, C. & Knight, R. Human-associated microbial signatures: Examining their predictive value. Cell Host and Microbe. 10, 292- 296 (2011). 126. Yazdani, M., Taylor, B.C., Debelius, J., Li, W., Knight, R., Smarr, L. Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease. IEEE. 2016 IEEE International Conference on Big Data. 1272-1280. (2016). 127. Huang, S. et al. Predictive modeling of gingivitis severity and susceptibility via oral microbiota. ISME J. 8, 1768-1780 (2014). 128. Teng, F. et al. Prediction of Early Childhood Caries via Spatial-Temporal Variations of Oral Microbiota. Cell Host Microbe. 18, 296-306 (2015). 129.
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- Authors
- Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., 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. G., & Dorrestein, P. C.
- Affiliations
- University of California San Diego (US)
- Ghent University (BE)
- NOAA National Marine Fisheries Service (US)
- University of Washington Bothell (US)
- Northern Arizona University (US)
- Corresponding author
- Knight, Rob
- ORCID
- Knight, Rob
- Vrbanac, Alison
- Taylor, Bryn C.
- Aksenov, Alexander
- Callewaert, Chris
- Debelius, Justine
- Kosciolek, Tomasz
- McCall, Laura-Isobel
- McDonald, Daniel
- Melnik, Alexey V.
- Morton, James T.
- Quinn, Robert A.
- Sanders, Jon G.
- Swafford, Austin D.
- Thompson, Luke R.
- Tripathi, Anupriya
- Xu, Zhenjiang Z.
- Zaneveld, Jesse R.
- Zhu, Qiyun
- Caporaso, J. Gregory
- Dorrestein, Pieter C.
- Venue
- Nature Reviews Microbiology
- Published
- 2018-05-23
- Volume
- 16
- Issue
- 7
- Pages
- 410-422
- ISSN
- 1740-1526 (ISSN-L)
- 1740-1534
- OpenAlex
- W2803457956
- Cited by
- 2,030
- References
- 20
- Citations by year
- 2026: 438 · 2025: 241 · 2024: 199 · 2023: 207 · 2022: 243 · 2021: 302 · 2020: 223 · 2019: 138 · 2018: 36 · 2013: 1
- FWCI
- 17.46
- Type
- journal article
- Language
- en
- Open access
- Locations
- Nature Reviews Microbiology (publishedVersion)
- PubMed (publishedVersion)
- NOAA Institutional Repository (submittedVersion)
- Aquila Digital Community (University of Southern Mississippi) (submittedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Funders
- National Science Foundation
- Alfred P. Sloan Foundation
- W. M. Keck Foundation
- John Templeton Foundation
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- microbiome analysis, experimental design, metagenomics, metabolomics, multi-omics integration, operational taxonomic units, compositional data analysis, false discovery rate, research reproducibility, microbial communities
- MeSH terms
- Animals, Bacteria, Environmental Microbiology, Humans, Reproducibility of Results, Metagenomics, Microbiota
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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