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Bayesian community-wide culture-independent microbial source tracking

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Abstract

Contamination is a critical issue in high-throughput metagenomic studies, yet progress toward a comprehensive solution has been limited. We present SourceTracker, a Bayesian approach to estimate the proportion of contaminants in a given community that come from possible source environments. We applied SourceTracker to microbial surveys from neonatal intensive care units (NICUs), offices and molecular biology laboratories, and provide a database of known contaminants for future testing.

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Figure 1: Comparison of SourceTracker and other models.
Figure 2: SourceTracker proportion estimates for a subset of sink samples.
Figure 3: Relative abundance of common contaminating operational taxonomic units (OTUs).

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Acknowledgements

We acknowledge funding from US National Institutes of Health (R01HG4872, R01HG4866, U01HL098957 and P01DK78669), the Crohn's and Colitis Foundation of America and the Howard Hughes Medical Institute, and B. Prithiviraj for helpful insight into previous related work.

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Authors and Affiliations

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

    Dan Knights & Michael C Mozer

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

    Justin Kuczynski & Jesse Zaneveld

  3. Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA

    Emily S Charlson, Ronald G Collman & Frederic D Bushman

  4. Department of Microbiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA

    Emily S Charlson

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

    Rob Knight

  6. Howard Hughes Medical Institute, Chevy Chase, Maryland, USA.,

    Rob Knight

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

    Scott T Kelley

Authors
  1. Dan Knights
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  2. Justin Kuczynski
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  3. Emily S Charlson
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  4. Jesse Zaneveld
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  5. Michael C Mozer
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  6. Ronald G Collman
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  7. Frederic D Bushman
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  8. Rob Knight
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  9. Scott T Kelley
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Contributions

D.K. designed the algorithm and software, and performed computational experiments; D.K., R.K. and S.T.K. wrote the manuscript; J.K., E.S.C., J.Z., M.C.M., R.G.C. and F.D.B. contributed to writing the manuscript; J.K. and M.C.M. contributed to algorithm design; J.K. processed the data after sequencing; E.S.C. collected the data; R.G.C. and F.D.B. organized and supervised the data collection; R.G.C., F.D.B., R.K. and S.T.K. supervised the project.

Corresponding author

Correspondence to Scott T Kelley.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 and Supplementary Tables 1–2 (PDF 3401 kb)

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Knights, D., Kuczynski, J., Charlson, E. et al. Bayesian community-wide culture-independent microbial source tracking. Nat Methods 8, 761–763 (2011). https://doi.org/10.1038/nmeth.1650

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  • DOI: https://doi.org/10.1038/nmeth.1650

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