Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Bayesian community-wide culture-independent microbial source tracking


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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

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).


  1. Acinas, S.G., Sarma-Rupavtarm, R., Klepac-Ceraj, V. & Polz, M.F. Appl. Environ. Microbiol. 71, 8966–8969 (2005).

    Article  CAS  Google Scholar 

  2. Quince, C. et al. Nat. Methods 6, 639–641 (2009).

    Article  CAS  Google Scholar 

  3. Tanner, M.A., Goebel, B.M., Dojka, M.A. & Pace, N.R. Appl. Environ. Microbiol. 64, 3110–3113 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Simpson, J.M., Santo Domingo, J.W. & Reasoner, D.J. Environ. Sci. Technol. 36, 5279–5288 (2002).

    Article  CAS  Google Scholar 

  5. Wu, C.H. et al. PLoS ONE 5, e11285 (2010).

    Article  Google Scholar 

  6. Greenberg, J., Price, B. & Ware, A. Water Res. 44, 2629–2637 (2010).

    Article  CAS  Google Scholar 

  7. Smith, A., Sterba-Boatwright, B. & Mott, J. Water Res. 44, 4067–4076 (2010).

    Article  CAS  Google Scholar 

  8. Dufrêne, M. & Legendre, P. Ecol. Monogr. 67, 345–366 (1997).

    Google Scholar 

  9. Costello, E.K. et al. Science 326, 1694–1697 (2009).

    Article  CAS  Google Scholar 

  10. Lauber, C.L., Hamady, M., Knight, R. & Fierer, N. Appl. Environ. Microbiol. 75, 5111–5120 (2009).

    Article  CAS  Google Scholar 

  11. Lozupone, C. & Knight, R. Appl. Environ. Microbiol. 71, 8228–8235 (2005).

    Article  CAS  Google Scholar 

  12. Blei, D.M., Ng, A.Y. & Jordan, M.I. J. Mach. Learn. 3, 993–1022 (2003).

    Google Scholar 

  13. Griffiths, T.L. & Steyvers, M. Proc. Natl. Acad. Sci. USA 101, 5228–5235 (2004).

    Article  CAS  Google Scholar 

  14. Wang, Q., Garrity, G.M., Tiedje, J.M. & Cole, J.R. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

    Article  CAS  Google Scholar 

  15. Fierer, N., Hamady, M., Lauber, C.L. & Knight, R. Proc. Natl. Acad. Sci. USA 105, 17994–17999 (2008).

    Article  CAS  Google Scholar 

  16. Wu, G.D. et al. BMC Microbiol. 10, 206 (2010).

    Article  Google Scholar 

  17. Caporaso, J.G. et al. Nat. Methods 7, 335–336 (2010).

    Article  CAS  Google Scholar 

  18. Reeder, J. & Knight, R. Nat. Methods 7, 668–669 (2010).

    Article  CAS  Google Scholar 

  19. Edgar, R.C. Bioinformatics 26, 2460–2461 (2010).

    Article  CAS  Google Scholar 

  20. Haas, B.J. et al. Genome Res. 21, 494–504 (2011).

    Article  CAS  Google Scholar 

  21. Price, M.N., Dehal, P.S. & Arkin, A.P. Mol. Biol. Evol. 26, 1641–1650 (2009).

    Article  CAS  Google Scholar 

  22. Breiman, L. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

Download references


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.

Author information

Authors and Affiliations



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.

Ethics declarations

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)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Knights, D., Kuczynski, J., Charlson, E. et al. Bayesian community-wide culture-independent microbial source tracking. Nat Methods 8, 761–763 (2011).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing