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.

A guide through present computational approaches for the identification of mammalian microRNA targets

Nature Methods volume 3, pages 881–886 (2006)Cite this article

Abstract

Computational microRNA (miRNA) target prediction is a field in flux. Here we present a guide through five widely used mammalian target prediction programs. We include an analysis of the performance of these individual programs and of various combinations of these programs. For this analysis we compiled several benchmark data sets of experimentally supported miRNA–target gene interactions. Based on the results, we provide a discussion on the status of target prediction and also suggest a stepwise approach toward predicting and selecting miRNA targets for experimental testing.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

Buy

All prices are NET prices.

Figure 1: Three categories of microRNA target sites.
Figure 2: Performance spectrum of target prediction programs.

References

  1. Ambros, V. The functions of animal microRNAs. Nature 431, 350–355 (2004).

    Article  CAS  Google Scholar 

  2. Bartel, D.P. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297 (2004).

    Article  CAS  Google Scholar 

  3. Lagos-Quintana, M., Rauhut, R., Lendeckel, W. & Tuschl, T. Identification of novel genes coding for small expressed RNAs. Science 294, 853–858 (2001).

    Article  CAS  Google Scholar 

  4. Mourelatos, Z. et al. miRNPs: a novel class of ribonucleoproteins containing numerous microRNAs. Genes Dev. 16, 720–728 (2002).

    Article  CAS  Google Scholar 

  5. Lee, R.C. & Ambros, V. An extensive class of small RNAs in Caenorhabditis elegans. Science 294, 862–864 (2001).

    Article  CAS  Google Scholar 

  6. Lau, N.C., Lim, L.P., Weinstein, E.G. & Bartel, D.P. An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294, 858–862 (2001).

    Article  CAS  Google Scholar 

  7. Du, T. & Zamore, P.D. microPrimer: the biogenesis and function of microRNA. Development 132, 4645–4652 (2005).

    Article  CAS  Google Scholar 

  8. Kim, V.N. & Nam, J.W. Genomics of microRNA. Trends Genet. 22, 165–173 (2006).

    Article  CAS  Google Scholar 

  9. Stark, A., Brennecke, J., Russell, R.B. & Cohen, S.M. Identification of Drosophila MicroRNA targets. PLoS Biol. 1, E60 (2003).

    Article  Google Scholar 

  10. Rajewsky, N. & Socci, N.D. Computational identification of microRNA targets. Dev. Biol. 267, 529–535 (2004).

    Article  CAS  Google Scholar 

  11. Enright, A.J. et al. MicroRNA targets in Drosophila. Genome Biol. 5, R1 (2003).

    Article  Google Scholar 

  12. Kiriakidou, M. et al. A combined computational-experimental approach predicts human microRNA targets. Genes Dev. 18, 1165–1178 (2004).

    Article  CAS  Google Scholar 

  13. Lewis, B.P., Shih, I.H., Jones-Rhoades, M.W., Bartel, D.P. & Burge, C.B. Prediction of mammalian microRNA targets. Cell 115, 787–798 (2003).

    Article  CAS  Google Scholar 

  14. Lewis, B.P., Burge, C.B. & Bartel, D.P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15–20 (2005).

    Article  CAS  Google Scholar 

  15. Krek, A. et al. Combinatorial microRNA target predictions. Nat. Genet. 37, 495–500 (2005).

    Article  CAS  Google Scholar 

  16. Brennecke, J., Stark, A., Russell, R.B. & Cohen, S.M. Principles of microRNA-target recognition. PLoS Biol. 3, e85 (2005).

    Article  Google Scholar 

  17. Lall, S. et al. A genome-wide map of conserved microRNA targets in C. elegans. Curr. Biol. 16, 460–471 (2006).

    Article  CAS  Google Scholar 

  18. John, B. et al. Human MicroRNA targets. PLoS Biol. 2, e363 (2004).

    Article  Google Scholar 

  19. Rajewsky, N. microRNA target predictions in animals. Nat. Genet. 38 (Suppl.), S8–S13 (2006).

    Article  CAS  Google Scholar 

  20. Griffiths-Jones, S., Grocock, R.J., van Dongen, S., Bateman, A. & Enright, A.J. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 34, D140–D144 (2006).

    Article  CAS  Google Scholar 

  21. Sethupathy, P., Corda, B. & Hatzigeorgiou, A.G. TarBase: A comprehensive database of experimentally supported animal microRNA targets. RNA 12, 192–197 (2006).

    Article  CAS  Google Scholar 

  22. Stark, A., Brennecke, J., Bushati, N., Russell, R.B. & Cohen, S.M. Animal microRNAs confer robustness to gene expression and have a significant impact on 3′UTR evolution. Cell 123, 1133–1146 (2005).

    Article  CAS  Google Scholar 

  23. Gupta, A., Gartner, J.J., Sethupathy, P., Hatzigeorgiou, A.G. & Fraser, N.W. Anti-apoptotic function of a microRNA encoded by the HSV-1 latency-associated transcript. Nature 442, 82–85 (2006).

    Article  CAS  Google Scholar 

  24. Vella, M.C., Reinert, K. & Slack, F.J. Architecture of a validated microRNA:target interaction. Chem. Biol. 11, 1619–1623 (2004).

    Article  CAS  Google Scholar 

  25. Didiano, D. & Hobert, O. Perfect seed pairing is not a generally reliable predictor for miRNA-target interactions. Nat. Struct. Mol. Biol. 13, 849–851 (2006).

    Article  CAS  Google Scholar 

  26. Farh, K.K. et al. The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science 310, 1817–1821 (2005).

    Article  CAS  Google Scholar 

  27. Chan, C.S., Elemento, O. & Tavazoie, S. Revealing posttranscriptional regulatory elements through network-level conservation. PLoS Comput. Biol. 1, e69 (2005).

    Article  Google Scholar 

  28. Megraw, M. et al. MicroRNA promoter element discovery in Arabidopsis. RNA 12, 1612–1619 (2006).

    Article  CAS  Google Scholar 

  29. Xie, X. et al. Systematic discovery of regulatory motifs in human promoters and 3′ UTRs by comparison of several mammals. Nature 434, 338–345 (2005).

    Article  CAS  Google Scholar 

  30. Bentwich, I. et al. Identification of hundreds of conserved and nonconserved human microRNAs. Nat. Genet. 37, 766–770 (2005).

    Article  CAS  Google Scholar 

  31. Berezikov, E. et al. Phylogenetic shadowing and computational identification of human microRNA genes. Cell 120, 21–24 (2005).

    Article  CAS  Google Scholar 

  32. Rusinov, V., Baev, V., Minkov, I.N. & Tabler, M. MicroInspector: a web tool for detection of miRNA binding sites in an RNA sequence. Nucleic Acids Res. 33 (Web server issue), W696–700 (2005).

    Article  CAS  Google Scholar 

  33. Grun, D., Wang, Y.L., Langenberger, D., Gunsalus, K.C. & Rajewsky, N. microRNA target predictions across seven Drosophila species and comparison to mammalian targets. PLoS Comput. Biol. 1, e13 (2005).

    Article  Google Scholar 

  34. Rehmsmeier, M., Steffen, P., Hochsmann, M. & Giegerich, R. Fast and effective prediction of microRNA/target duplexes. RNA 10, 1507–1517 (2004).

    Article  CAS  Google Scholar 

  35. Saetrom, O., Snove, O., Jr. & Saetrom, P. Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms. RNA 11, 995–1003 (2005).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful for the insightful suggestions regarding this study from the reviewers and many of our colleagues. We thank A. Economides, M. Reczko and K. Essien for their helpful comments on the manuscript, and J. Hirel for his help with the extraction of precompiled target predictions from the web. P.S. is supported by a pre-doctoral US National Institutes of Health training grant (5T32GM008216). P.S., M.M. and A.G.H. are supported by a US National Science Foundation Career Award Grant (DBI-0238295).

Author information

Authors and Affiliations

  1. Center for Bioinformatics, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA

    Praveen Sethupathy, Molly Megraw & Artemis G Hatzigeorgiou

  2. Department of Genetics, School of Medicine, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA

    Praveen Sethupathy, Molly Megraw & Artemis G Hatzigeorgiou

  3. Department of Computer and Information Science, School of Engineering, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA

    Artemis G Hatzigeorgiou

Authors
  1. Praveen Sethupathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Molly Megraw
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Artemis G Hatzigeorgiou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Artemis G Hatzigeorgiou.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

Non-conserved TarBase miRNA-target interactions. (XLS 16 kb)

Supplementary Table 2

Benchmark datasets. (XLS 30 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Sethupathy, P., Megraw, M. & Hatzigeorgiou, A. A guide through present computational approaches for the identification of mammalian microRNA targets. Nat Methods 3, 881–886 (2006). https://doi.org/10.1038/nmeth954

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth954

This article is cited by

Search

Advanced search

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