Bayesian community-wide culture-independent microbial source tracking (original) (raw)

• Brief Communication
• Published: 17 July 2011
• Justin Kuczynski2,
• Emily S Charlson3,4,
• Jesse Zaneveld2,
• Michael C Mozer1,
• Ronald G Collman3,
• Frederic D Bushman3,
• Rob Knight5,6 &
• …
• Scott T Kelley7

Nature Methods volume 8, pages 761–763 (2011)Cite this article

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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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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 toScott T Kelley.

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The authors declare no competing financial interests.

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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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• Received: 10 February 2011
• Accepted: 02 June 2011
• Published: 17 July 2011
• Issue Date: September 2011
• DOI: https://doi.org/10.1038/nmeth.1650

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