Skip to main content

Thank you for visiting nature.com. 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.

  • Brief Communication
  • Published:

MicroRNA target site identification by integrating sequence and binding information

Nature Methods volume 10, pages 630–633 (2013)Cite this article

Subjects

Abstract

High-throughput sequencing has opened numerous possibilities for the identification of regulatory RNA-binding events. Cross-linking and immunoprecipitation of Argonaute proteins can pinpoint a microRNA (miRNA) target site within tens of bases but leaves the identity of the miRNA unresolved. A flexible computational framework, microMUMMIE, integrates sequence with cross-linking features and reliably identifies the miRNA family involved in each binding event. It considerably outperforms sequence-only approaches and quantifies the prevalence of noncanonical binding modes.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Figure 1: Identifying miRNA target sites by a joint sequence-interaction model.
Figure 2: Validation of predicted sites and their impact on expression.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

Referenced accessions

Gene Expression Omnibus

References

  1. Jiang, Q. et al. Nucleic Acids Res. 37, D98–D104 (2009).

    Article  CAS  Google Scholar 

  2. Hendrickson, D.G., Hogan, D.J., Herschlag, D., Ferrell, J.E. & Brown, P.O. PLoS ONE 3, e2126 (2008).

    Article  Google Scholar 

  3. Chi, S.W., Zang, J.B., Mele, A. & Darnell, R.B. Nature 460, 479–486 (2009).

    Article  CAS  Google Scholar 

  4. Hafner, M. et al. Cell 141, 129–141 (2010).

    Article  CAS  Google Scholar 

  5. Sugimoto, Y. et al. Genome Biol. 13, R67 (2012).

    Article  Google Scholar 

  6. Corcoran, D.L. et al. Genome Biol. 12, R79 (2011).

    Article  CAS  Google Scholar 

  7. Bartel, D.P. Cell 136, 215–233 (2009).

    Article  CAS  Google Scholar 

  8. Baltz, A.G. et al. Mol. Cell 46, 674–690 (2012).

    Article  CAS  Google Scholar 

  9. Juang, B.H. & Rabiner, L.R. Technometrics 33, 251–272 (1991).

    Article  Google Scholar 

  10. Friedman, R.C., Farh, K.K.-H., Burge, C.B. & Bartel, D.P. Genome Res. 19, 92–105 (2009).

    Article  CAS  Google Scholar 

  11. Khorshid, M., Hausser, J., Zavolan, M. & van Nimwegen, E. Nat. Methods 10, 253–255 (2013).

    Article  CAS  Google Scholar 

  12. Skalsky, R.L. et al. PLoS Pathog. 8, e1002484 (2012).

    Article  CAS  Google Scholar 

  13. Kishore, S. et al. Nat. Methods 8, 559–564 (2011).

    Article  CAS  Google Scholar 

  14. Chi, S.W., Hannon, G.J. & Darnell, R.B. Nat. Struct. Mol. Biol. 19, 321–327 (2012).

    Article  CAS  Google Scholar 

  15. Shin, C. et al. Mol. Cell 38, 789–802 (2010).

    Article  CAS  Google Scholar 

  16. Brennecke, J., Stark, A., Russell, R.B. & Cohen, S.M. PLoS Biol. 3, e85 (2005).

    Article  Google Scholar 

  17. Leung, A.K. et al. Nat. Struct. Mol. Biol. 18, 237–244 (2011).

    Article  CAS  Google Scholar 

  18. Betel, D., Koppal, A., Agius, P., Sander, C. & Leslie, C. Genome Biol. 11, R90 (2010).

    Article  Google Scholar 

  19. Siepel, A. et al. Genome Res. 15, 1034–1050 (2005).

    Article  CAS  Google Scholar 

  20. Feederle, R. et al. J. Virol. 85, 9801–9810 (2011).

    Article  CAS  Google Scholar 

  21. Flicek, P. et al. Nucleic Acids Res. 38 (suppl. 1), D557–D562 (2010).

    Article  CAS  Google Scholar 

  22. Li, B. & Dewey, C.N. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  Google Scholar 

  23. Leng, N. et al. Bioinformatics 29, 1035–1043 (2013).

    Article  CAS  Google Scholar 

  24. Majoros, W.H. Methods for Computational Gene Prediction (Cambridge Univ. Press, 2007).

  25. Fariselli, P., Martelli, P.L. & Casadio, R. BMC Bioinformatics 6 (suppl. 4), S12 (2005).

    Article  Google Scholar 

  26. Lekprasert, P., Mayhew, M. & Ohler, U. PLoS ONE 6, e20622 (2011).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the US National Science Foundation (MCB-0822033) and US National Institutes of Health (NIH R01-GM104962) to U.O. and by awards from the NIH (R01-AI067968; R01-DA030086) to B.R.C. Contributions by R.L.S. were additionally supported by a Duke Center for AIDS Research small grant award (P30-AI064518). We thank S. Grosswendt and M. Piechotta for critical reading of a manuscript draft.

Author information

Author notes

  1. William H Majoros and Parawee Lekprasert: These authors contributed equally to this work.

Authors and Affiliations

  1. Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, USA

    William H Majoros, Parawee Lekprasert, Neelanjan Mukherjee, David L Corcoran & Uwe Ohler

  2. Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, USA

    Parawee Lekprasert

  3. Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany

    Neelanjan Mukherjee & Uwe Ohler

  4. Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina, USA

    Rebecca L Skalsky & Bryan R Cullen

  5. Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA

    Uwe Ohler

  6. Department of Biology, Humboldt University of Berlin, Berlin, Germany

    Uwe Ohler

Authors
  1. William H Majoros
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Parawee Lekprasert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Neelanjan Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rebecca L Skalsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David L Corcoran
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bryan R Cullen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Uwe Ohler
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

W.H.M. implemented the modeling framework and designed the models, P.L. and W.H.M. performed the computational experiments, P.L. investigated bulge sites, and N.M. and D.L.C. configured and ran the PARalyzer pipeline and provided guidance on analyzing its outputs. P.L., W.H.M. and U.O. analyzed the data. R.L.S., B.R.C. and N.M. designed and performed wet-lab experiments, and W.H.M., P.L. and U.O. wrote the manuscript.

Corresponding author

Correspondence to Uwe Ohler.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13 and Supplementary Tables 1–4 (PDF 2192 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Majoros, W., Lekprasert, P., Mukherjee, N. et al. MicroRNA target site identification by integrating sequence and binding information. Nat Methods 10, 630–633 (2013). https://doi.org/10.1038/nmeth.2489

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.2489

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

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