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Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing

Nature Methods volume 10, pages 57–59 (2013)Cite this article

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Abstract

High-throughput sequencing has revolutionized microbial ecology, but read quality remains a considerable barrier to accurate taxonomy assignment and α-diversity assessment for microbial communities. We demonstrate that high-quality read length and abundance are the primary factors differentiating correct from erroneous reads produced by Illumina GAIIx, HiSeq and MiSeq instruments. We present guidelines for user-defined quality-filtering strategies, enabling efficient extraction of high-quality data and facilitating interpretation of Illumina sequencing results.

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Figure 1
Figure 2: The α and β diversity comparisons of mock community reads filtered using select phred_quality_score (q) settings (data set 1).

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Acknowledgements

We thank G. Giannoukos (Broad Institute of MIT and Harvard), I. Rasolonjatovo (Illumina), M. Gebert (University of Colorado, Boulder) and L. Wegener Parfrey (University of Colorado, Boulder) for contributing mock community sequencing data used in this study, and S. Huse and A. Gonzalez for useful feedback and discussions of this manuscript. This work was supported in part by grants from the US National Institutes of Health (NIH DK78669 to J.I.G., NIH R01HD059127 to D.A.M. and NIH U54HG004969 to D.G.), the Juvenile Diabetes Research Fund (D.G.), the Crohn's and Colitis Foundation of America (J.I.G. and D.G.), and the Howard Hughes Medical Institute. N.A.B. was supported by the 2012–2013 Dannon Probiotics Fellow Program (The Dannon Company) and a Wine Spectator scholarship.

Author information

Authors and Affiliations

  1. Department of Viticulture and Enology, University of California, Davis, Davis, California, USA.,

    Nicholas A Bokulich & David A Mills

  2. Department of Food Science and Technology, University of California, Davis, Davis, California, USA.,

    Nicholas A Bokulich & David A Mills

  3. Foods for Health Institute, University of California, Davis, Davis, California, USA.,

    Nicholas A Bokulich & David A Mills

  4. Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri, USA

    Sathish Subramanian, Jeremiah J Faith & Jeffrey I Gordon

  5. Microbial Systems & Communities, Genome Sequencing and Analysis Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Dirk Gevers

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

    Rob Knight

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

    Rob Knight

  8. Institute for Genomics and Systems Biology, Argonne National Laboratory, Argonne, Illinois, USA

    J Gregory Caporaso

  9. Department of Computer Science, Northern Arizona University, Flagstaff, Arizona, USA

    J Gregory Caporaso

Authors
  1. Nicholas A Bokulich
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  2. Sathish Subramanian
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  3. Jeremiah J Faith
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  4. Dirk Gevers
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  5. Jeffrey I Gordon
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  6. Rob Knight
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  7. David A Mills
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  8. J Gregory Caporaso
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Contributions

N.A.B., J.G.C., D.A.M. and R.K. conceived and designed the experiments; N.A.B. performed the experiments and data analysis. All authors contributed sequencing data sets and wrote the manuscript.

Corresponding author

Correspondence to J Gregory Caporaso.

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Competing interests

The authors declare no competing financial interests.

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Supplementary Text and Figures

Supplementary Figures 1–16, Supplementary Tables 1–9, Supplementary Note (PDF 21952 kb)

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Bokulich, N., Subramanian, S., Faith, J. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods 10, 57–59 (2013). https://doi.org/10.1038/nmeth.2276

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