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.

Detecting overlapping protein complexes in protein-protein interaction networks


We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a high extent of functional homogeneity.

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

Get just this article for as long as you need it


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

Figure 1: Benchmark results.


  1. Enright, A.J., van Dongen, S. & Ouzounis, C.A. Nucleic Acids Res. 30, 1575–1584 (2002).

    Article  CAS  Google Scholar 

  2. King, A., Pržulj, N. & Jurisica, I. Bioinformatics 20, 3013–3020 (2004).

    Article  CAS  Google Scholar 

  3. Pu, S., Wong, J., Turner, B., Cho, E. & Wodak, S. Nucleic Acids Res. 37, 825–831 (2009).

    Article  CAS  Google Scholar 

  4. Bader, G.D. & Hogue, C.W. BMC Bioinformatics 4, 2 (2003).

    Article  Google Scholar 

  5. Liu, G., Wong, L. & Chua, H.N. Bioinformatics 25, 1891–1897 (2009).

    Article  CAS  Google Scholar 

  6. Gavin, A. et al. Nature 440, 631–636 (2006).

    Article  CAS  Google Scholar 

  7. Krogan, N. et al. Nature 440, 637–643 (2006).

    Article  CAS  Google Scholar 

  8. Collins, S.R. et al. Mol. Cell. Proteomics 6, 439–450 (2007).

    Article  CAS  Google Scholar 

  9. Stark, C. et al. Nucleic Acids Res. 34, D535–D539 (2006).

    Article  CAS  Google Scholar 

  10. Frey, B.J. & Dueck, D. Science 315, 972–976 (2007).

    Article  CAS  Google Scholar 

  11. Palla, G., Derényi, I., Farkas, I. & Vicsek, T. Nature 435, 814–818 (2005).

    Article  CAS  Google Scholar 

  12. Macropol, K., Can, T. & Singh, A. BMC Bioinformatics 10, 283 (2009).

    Article  Google Scholar 

  13. Mewes, H.W. et al. Nucleic Acids Res. 32, D41–D44 (2004).

    Article  CAS  Google Scholar 

  14. Brohée, S. & van Helden, J. BMC Bioinformatics 7, 488 (2006).

    Article  Google Scholar 

  15. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V. & Parisi, D. Proc. Natl. Acad. Sci. USA 101, 2658–2663 (2004).

    Article  CAS  Google Scholar 

  16. Jansen, R. & Gerstein, M. Curr. Opin. Microbiol. 7, 535–545 (2004).

    Article  CAS  Google Scholar 

  17. Jansen, R. et al. Science 302, 449–453 (2003).

    Article  CAS  Google Scholar 

  18. Friedel, C.C., Krumsiek, J. & Zimmer, R. J. Comput. Biol. 16, 971–987 (2009).

    Article  CAS  Google Scholar 

  19. Huh, W.-K.K. et al. Nature 425, 686–691 (2003).

    Article  CAS  Google Scholar 

  20. Benjamini, Y. & Hochberg, Y. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  21. Hong, E. et al. Nucleic Acids Res. 36, D577–D581 (2008).

    Article  CAS  Google Scholar 

  22. Ashburner, M. et al. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  Google Scholar 

  23. Dwight, S. et al. Nucleic Acids Res. 30, 69–72 (2002).

    Article  CAS  Google Scholar 

  24. Shannon, P. et al. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  Google Scholar 

  25. Krumsiek, J., Friedel, C.C. & Zimmer, R. Bioinformatics 24, 2115–2116 (2008).

    Article  CAS  Google Scholar 

Download references


T.N. was supported by the Newton International Fellowship Scheme of the Royal Society grant NF080750. A.P. was supported by the Biotechnology and Biological Sciences Research Council New Investigator grant BB/F00964X/1. H.Y. was supported by US National Institute of General Medical Sciences grant R01 GM097358.

Author information

Authors and Affiliations



T.N. and A.P. conceived the study. T.N. devised and implemented the algorithm and conducted benchmarks. H.Y. evaluated the biological relevance of the results. A.P. supervised the project. H.Y., T.N. and A.P. discussed the results and implications. A.P. and T.N. wrote the manuscript.

Corresponding authors

Correspondence to Haiyuan Yu or Alberto Paccanaro.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8, Supplementary Tables 1–4, Supplementary Discussion (PDF 1310 kb)

Supplementary Data 1

Input data files used in the benchmarks. (ZIP 525 kb)

Supplementary Data 2

Gold standard data files used in the benchmarks. (ZIP 17 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Nepusz, T., Yu, H. & Paccanaro, A. Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods 9, 471–472 (2012).

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