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QIIME allows analysis of high-throughput community sequencing data

Nature Methods volume 7, pages 335–336 (2010)Cite this article

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To the Editor: High-throughput sequencing is revolutionizing microbial ecology studies. Efforts like the Human Microbiome Projects1 and the US National Ecological Observatory Network2 are helping us to understand the role of microbial diversity in habitats within our own bodies and throughout the planet.

Pyrosequencing using error-correcting, sample-specific barcodes allows hundreds of communities to be analyzed simultaneously in multiplex3. Integrating information from thousands of samples, including those obtained from time series, can reveal large-scale patterns that were inaccessible with lower-throughput sequencing methods. However, a major barrier to achieving such insights has been the lack of software that can handle these increasingly massive datasets. Although tools exist to perform library demultiplexing and taxonomy assignment4,5, tools for downstream analyses are scarce.

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Figure 1: QIIME analyses of the distal gut microbiotas of conventionally raised and conventionalized mice, gnotobiotic mice colonized with a human fecal gut microbiota (H-mice), and human adult mono- and dizygotic twins.

References

  1. National Institutes of Health Human Microbiome Project Working Group et al. Genome Res. 19, 2317–2323 (2009).

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Acknowledgements

We thank our collaborators for their helpful suggestions on features, documentation and the manuscript, and our funding agencies for their commitment to open-source software. This work was supported in part by Howard Hughes Medical Institute and grants from the Crohn's and Colitis Foundation of America, the German Academic Exchange Service, the Bill and Melinda Gates Foundation, the Colorado Center for Biofuels and Biorefining and the US National Institutes of Health (DK78669, GM65103, GM8759, HG4872 and its ARRA supplement, HG4866, DK83981 and LM9451).

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Author notes

  1. J Gregory Caporaso, Justin Kuczynski and Jesse Stombaugh: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado, USA

    J Gregory Caporaso, Jesse Stombaugh, Elizabeth K Costello, Catherine A Lozupone, Daniel McDonald, Meg Pirrung, Jens Reeder, Jeremy Widmann & Rob Knight

  2. Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, Colorado, USA

    Justin Kuczynski, William A Walters & Jesse Zaneveld

  3. Department of Microbiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Kyle Bittinger & Frederic D Bushman

  4. Cooperative Institute for Research in Environmental Sciences and Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA

    Noah Fierer

  5. Department of Computer Science, University of Colorado, Boulder, Colorado, USA

    Antonio Gonzalez Peña, Julia K Goodrich & Dan Knights

  6. Center for Genome Sciences, Washington University School of Medicine, St. Louis, Missouri, USA

    Jeffrey I Gordon, Brian D Muegge, Peter J Turnbaugh & Tanya Yatsunenko

  7. Computational Genomics Laboratory, John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia.,

    Gavin A Huttley

  8. Department of Biology, San Diego State University, San Diego, California, USA

    Scott T Kelley

  9. Department of Microbiology, Cornell University, Ithaca, New York, USA

    Jeremy E Koenig & Ruth E Ley

  10. Luca Technologies, Golden, Colorado, USA

    Joel R Sevinsky

  11. Howard Hughes Medical Institute, Boulder, Colorado, USA

    Rob Knight

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Correspondence to Rob Knight.

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Competing interests

J.R.S. is an employee of Luca Technologies.

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Supplementary Figure 1, Supplementary Table 1, Supplementary Discussion (PDF 343 kb)

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Caporaso, J., Kuczynski, J., Stombaugh, J. et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7, 335–336 (2010). https://doi.org/10.1038/nmeth.f.303

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