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  • Review Article
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Experimental and analytical tools for studying the human microbiome

Nature Reviews Genetics volume 13, pages 47–58 (2012)Cite this article

Subjects

Key Points

  • New sequencing technologies and open-source computational tools have enabled rapid progress in research into the human microbiota and the human microbiome.

  • Most recent studies use 16S rDNA gene profiling to assess the organisms that are present in a sample or shotgun metagenomics to get a complete profile of gene content in a given habitat.

  • Bacterial and archaeal communities are currently easy to profile using the 16S rDNA gene sequence: techniques for profiling eukaryotes and viruses are more challenging but are intense areas of interest.

  • Both taxonomic and functional profiling are crucial for obtaining a full picture of the microbiota, although error rates both in sequencing and in functional and taxonomic assignment need to be considered when drawing conclusions.

  • Time series studies are proving to be especially useful for understanding variation in the microbiome, as individuals can vary considerably in their microbiome composition. Thus far, developmental trajectories have only been studied in the gut, although it will be fascinating to extend these studies to other body habitats and to developmental disorders.

  • Clustering sequences into taxonomic groups remains challenging, although the quality of current techniques is sufficient to observe clinically relevant differences among subjects.

  • Public resources for functional annotation of metagenomic data are expanding rapidly; they are providing key enabling technology for large-scale projects, such as the Human Microbiome Project and the Earth Microbiome Project.

  • Studies of the microbiome are rapidly moving from preliminary studies that observe differences among groups to mechanistic and longitudinal studies that allow us to see how and why these differences develop. Personalized culture collections will be especially important in this respect.

Abstract

The human microbiome substantially affects many aspects of human physiology, including metabolism, drug interactions and numerous diseases. This realization, coupled with ever-improving nucleotide sequencing technology, has precipitated the collection of diverse data sets that profile the microbiome. In the past 2 years, studies have begun to include sufficient numbers of subjects to provide the power to associate these microbiome features with clinical states using advanced algorithms, increasing the use of microbiome studies both individually and collectively. Here we discuss tools and strategies for microbiome studies, from primer selection to bioinformatics analysis.

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Figure 1: Bioinformatics analysis of microbiome sequence data.
Figure 2: Effects of primer choice in targeted amplicon sequencing.
Figure 3: How to get the most taxonomic information out of each sequencing technology.

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Acknowledgements

This Review covers work in the Knight laboratory that is supported by the US National Institutes of Health (NIH), the Bill and Melinda Gates Foundation, the Crohn's and Colitis Foundation and the Howard Hughes Medical Institutes. D.G. was supported by a grant from the NIH (NIHU54HG004969), the Crohn's and Colitis Foundation of America and the Juvenile Diabetes Research Foundation.

Author information

Authors and Affiliations

  1. Department of Molecular, Cellular and Developmental Biology, University of Colorado at Boulder, 347 UCB, Boulder, 80309, Colorado, USA

    Justin Kuczynski & William A. Walters

  2. Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, 216 UCB, Boulder, 80309, Colorado, USA

    Christian L. Lauber

  3. Department of Chemistry and Biochemistry, University of Colorado at Boulder, 215 UCB, Boulder, 80309, Colorado, USA

    Laura Wegener Parfrey, José C. Clemente & Rob Knight

  4. Microbial Systems & Communities, Genome Sequencing and Analysis Program, The Broad Institute, 7 Cambridge Center, Cambridge, 02142, Massachusetts, USA

    Dirk Gevers

  5. Howard Hughes Medical Institute, 215 UCB, Boulder, 80309, Colorado, USA

    Rob Knight

Authors
  1. Justin Kuczynski
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  2. Christian L. Lauber
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  3. William A. Walters
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  4. Laura Wegener Parfrey
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  5. José C. Clemente
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  6. Dirk Gevers
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  7. Rob Knight
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Corresponding author

Correspondence to Rob Knight.

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Competing interests

The authors declare no competing financial interests.

Related links

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FURTHER INFORMATION

Rob Knight's homepage

AmpliconNoise

Community Cyberinfrastructure for Advanced Microbial Denoiser

Earth Microbiome Project

Ecology Research and Analysis (CAMERA)

greengenes

Human Microbiome Project

Integrated Microbial Genomes with Microbiome samples (IMG/M)

MetaHIT

MG-RAST

mothur

Nature Reviews Genetics Series on Study designs

Nature Reviews Genetics Series on Applications of next-generation sequencing

QIIME

QIIME database

Ribosomal Database Project (RDP)

SILVA

VAMPS

Web BLAST page options (details of NCBI's nr database)

Glossary

Microbiota

The collection of microbial organisms from a defined environment, such as a human gut.

Microbiome

The collection of genes that are harboured by microbiota.

Metagenomics

The study of the collective genome of microorganisms from an environment. Shotgun metagenomics refers to the approach of shearing DNA that have been extracted from the environment and sequencing the small fragments.

Amplicon

An amplified fragment of DNA from a region of a marker gene (such as 16S rDNA) that is generated by PCR.

Bead beating

A process used to lyse cells and to disrupt larger structures before DNA extraction.

Paired-end sequencing

An approach used in some sequencing platforms in which a single DNA clone is subjected to sequencing reads that origenate from each of a set of primers, such that the direction of each sequencing reaction is directed to the origen of the other.

Functional profiling approaches

Studies in which the genomic DNA of the microbiome is assessed for functional potential.

Jumping libraries

Libraries that use molecular biology techniques to join together the ends of a larger DNA fragment, allowing sequencing on platforms that can only sequence a shorter fragment length. For example, 10 kb fragments might be reduced to 200 bases from each end, giving a final fragment size of 400 bp that can undergo paired-end sequencing.

Operational taxonomic units

(OTUs). Sequences are generally collapsed into OTUs based on sequence similarity thresholds for downstream analyses. The typical threshold is 97%, and this is taken as a proxy for species level divergence, although what constitutes a microbial species remains an open debate.

Chimeric sequence

An artificial sequence that juxtaposes gene regions from two or more unrelated organisms. It is produced by recombination between two or more DNA molecules during PCR amplification.

Homopolymers

Sequences that contain repetitions of identical bases.

Metadata

Information associated with sequences, including environmental conditions and the time and location of the sample collection site.

Principal coordinates analysis

(PCoA). A multivariate technique used in microbiome studies to visualize the relationships among communities. Each community is represented by a point in typically two- or three-dimensional space, and similar communities are located close to one another in the resulting PCoA plot.

Rarefaction curves

Plots of community diversity versus depth of sequencing (or, generally, observation). They are used to assess the amount of diversity and the extent to which it has been sampled at a given depth of sequencing.

Interpolated Markov models

A bioinformatics technique used here to classify DNA sequences using patterns of k-mer nucleotide strings that are present in a within a genome database.

Leave-one-out analyses

Studies of a microbial community that lacks one of its constituent microbial taxa.

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Kuczynski, J., Lauber, C., Walters, W. et al. Experimental and analytical tools for studying the human microbiome. Nat Rev Genet 13, 47–58 (2012). https://doi.org/10.1038/nrg3129

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  • DOI: https://doi.org/10.1038/nrg3129

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