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Metagenomic species profiling using universal phylogenetic marker genes


To quantify known and unknown microorganisms at species-level resolution using shotgun sequencing data, we developed a method that establishes metagenomic operational taxonomic units (mOTUs) based on single-copy phylogenetic marker genes. Applied to 252 human fecal samples, the method revealed that on average 43% of the species abundance and 58% of the richness cannot be captured by current reference genome–based methods. An implementation of the method is available at

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Figure 1: Phylogenetic marker gene–based mOTUs.
Figure 2: Phylogenetic analysis of mOTU linkage groups.
Figure 3: Performance and application of mOTU linkage groups.


  1. Klappenbach, J.A., Saxman, P.R., Cole, J.R. & Schmidt, T.M. Nucleic Acids Res. 29, 181–184 (2001).

    CAS  Article  Google Scholar 

  2. Engelbrektson, A. et al. ISME J. 4, 642–647 (2010).

    CAS  Article  Google Scholar 

  3. Claesson, M.J. et al. Nucleic Acids Res. 38, e200 (2010).

    Article  Google Scholar 

  4. Gevers, D. et al. Nat. Rev. Microbiol. 3, 733–739 (2005).

    CAS  Article  Google Scholar 

  5. Arumugam, M. et al. Nature 473, 174–180 (2011).

    CAS  Article  Google Scholar 

  6. Liu, B., Gibbons, T., Ghodsi, M., Treangen, T. & Pop, M. BMC Genomics 12 (suppl. 2), S4 (2011).

  7. Segata, N. et al. Nat. Methods 9, 811–814 (2012).

    CAS  Article  Google Scholar 

  8. Ciccarelli, F. et al. Science 311, 1283–1287 (2006).

    CAS  Article  Google Scholar 

  9. Sorek, R. et al. Science 318, 1449–1452 (2007).

    CAS  Article  Google Scholar 

  10. von Mering, C. et al. Science 315, 1126–1130 (2007).

    CAS  Article  Google Scholar 

  11. Mende, D.R., Sunagawa, S., Zeller, G. & Bork, P. Nat. Methods 10, 881–884 (2013).

    CAS  Article  Google Scholar 

  12. Qin, J. et al. Nature 464, 59–65 (2010).

    CAS  Article  Google Scholar 

  13. The Human Microbiome Project Consortium. Nature 486, 215–221 (2012).

  14. Nelson, K.E. et al. Science 328, 994–999 (2010).

    CAS  PubMed  Google Scholar 

  15. Walker, A.W. et al. ISME J. 5, 220–230 (2011).

    CAS  Article  Google Scholar 

  16. Mondot, S. et al. Inflamm. Bowel Dis. 17, 185–192 (2011).

    CAS  Article  Google Scholar 

  17. Schloissnig, S. et al. Nature 493, 45–50 (2013).

    Article  Google Scholar 

  18. Turnbaugh, P.J. et al. Nature 457, 480–484 (2009).

    CAS  Article  Google Scholar 

  19. Rajilic-Stojanovic, M., Heilig, H.G., Tims, S., Zoetendal, E.G. & de Vos, W.M. Environ. Microbiol. 15, 1146–1159 (2012).

    Article  Google Scholar 

  20. Manichanh, C., Borruel, N., Casellas, F. & Guarner, F. Nat. Rev. Gastroenterol. Hepatol. 9, 599–608 (2012).

    CAS  Article  Google Scholar 

  21. Rajilic-Stojanovic, M., Shanahan, F., Guarner, F. & de Vos, W.M. Inflamm. Bowel Dis. 19, 481–488 (2013).

    Article  Google Scholar 

  22. Png, C.W. et al. Am. J. Gastroenterol. 105, 2420–2428 (2010).

    CAS  Article  Google Scholar 

  23. Qin, J. et al. Nature 490, 55–60 (2012).

    CAS  Article  Google Scholar 

  24. Forslund, K. et al. Genome Res. 23, 1163–1169 (2013).

    CAS  Article  Google Scholar 

  25. Kultima, J.R. et al. PLoS ONE 7, e47656 (2012).

    Article  Google Scholar 

  26. Eddy, S.R. PLoS Comput. Biol. 7, e1002195 (2011).

    CAS  Article  Google Scholar 

  27. Muller, J., Creevey, C.J., Thompson, J.D., Arendt, D. & Bork, P. Bioinformatics 26, 263–265 (2010).

    CAS  Article  Google Scholar 

  28. Powell, S. et al. Nucleic Acids Res. 40, D284–D289 (2012).

    CAS  Article  Google Scholar 

  29. Mende, D.R. et al. PLoS ONE 7, e31386 (2012).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  31. Stamatakis, A. Bioinformatics 22, 2688–2690 (2006).

    CAS  Article  Google Scholar 

  32. Stamatakis, A. & Aberer, A. in Proceedings of the 2013 IEEE 27th International Symposium on Parallel and Distributed Processing 1195–1204 (2013).

  33. Berger, S.A. & Stamatakis, A. Bioinformatics 27, 2068–2075 (2011).

    CAS  Article  Google Scholar 

  34. Berger, S.A., Krompass, D. & Stamatakis, A. Syst. Biol. 60, 291–302 (2011).

    Article  Google Scholar 

  35. Letunic, I. & Bork, P. Bioinformatics 23, 127–128 (2007).

    CAS  Article  Google Scholar 

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We thank members of the European Molecular Biology Laboratory Information Technologies core facility and Y. Yuan for managing the high-performance computing resources and the members of the Bork group for fruitful discussions. This work was supported by funding from European Molecular Biology Laboratory, the European Community's Seventh Framework Programme via the MetaHIT (HEALTH-F4-2007-201052) and International Human Microbiome Standards, (HEALTH-F4-2010-261376) grants, The Novo Nordisk Foundation, The Lundbeck Foundation, institutional funding by the Heidelberg Institute for Theoretical Studies and Deutsche Forschungsgemeinschaft grants STA 860/2 and STA 860/3, the Metagenopolis ANR-11-DPBS-0001 grant, and the European Research Council Advanced Grants (MicrobesInside and CancerBiome grant agreement numbers 250172 and 268985 to W.M.d.V. and P.B., respectively).

Author information

Authors and Affiliations



P.B. and S.S. conceived the study, S.S., D.R.M., G.Z., F.I.-C., S.A.B., M.A., J.T. and A.S. designed and performed the analyses, S.S., D.R.M., G.Z., J.R.K., L.P.C. and J.L. developed and implemented the program, O.P., F.G., J.D. and J.W. provided data, S.S., D.R.M., G.Z. and P.B. wrote the manuscript, and M.A., J.T., H.B.N., S.R., O.P., F.G., W.M.d.V., S.D.E. and A.S. gave conceptual advice and revised the manuscript.

Corresponding author

Correspondence to Peer Bork.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8, and Supplementary Tables 4, 5 and 7 (PDF 2053 kb)

Supplementary Table 1

Prokaryotic reference genomes used in this study. (XLSX 159 kb)

Supplementary Table 2

Summary of benchmark results for speed and accuracy of marker gene identification. (XLSX 13 kb)

Supplementary Table 3

Metagenomic data sets used in this study. (XLSX 21 kb)

Supplementary Table 6

Summary of mOTU linkage groups with taxonomic annotations. (XLSX 67 kb)

Supplementary Software

mOTU profiling tool. (ZIP 86115 kb)

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Sunagawa, S., Mende, D., Zeller, G. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat Methods 10, 1196–1199 (2013).

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