Skip to main content
SpringerLink
Log in

Computational Prediction of MicroRNA Function and Activity

  • Protocol
  • First Online:
miRNomics: MicroRNA Biology and Computational Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1107))

Abstract

Inferring microRNA (miRNA) functions and activities has been extremely important to understand their system-level roles and the mechanisms behind the cellular behaviors of their target genes. This chapter first details methodologies necessary for prediction of function and activity. It then introduces the computational methods available for investigation of sequence and experimental data and for analysis of the information flow mediated through miRNAs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome- wide expression patterns. Proc Natl Acad Sci USA 95:14863–14868

    Article  PubMed  CAS  Google Scholar 

  2. Ben-Dor A, Shamir R, Yakhini Z (1999) Clustering gene expression patterns. J Comput Biol 6:281–297

    Article  PubMed  CAS  Google Scholar 

  3. Tompa M, Li N, Bailey TL et al (2005) Assessing computational tools for the discovery of transcription factor binding sites. Nat Biotechnol 23:137–144

    Article  PubMed  CAS  Google Scholar 

  4. Bartel DP (2004) MicroRNAs, genomics, biogenesis, mechanism, and function. Cell 116:281–297

    Article  PubMed  CAS  Google Scholar 

  5. Mendes ND, Freitas AT, Sagot MF (2009) Current tools for the identification of miRNA genes and their targets. Nucleic Acids Res 37:2419–2433

    Article  PubMed  CAS  Google Scholar 

  6. Saito T, Saetrom P (2010) MicroRNAs—targeting and target prediction. N Biotechnol 27:243–249

    Article  PubMed  CAS  Google Scholar 

  7. Alexiou P, Maragkakis M, Papadopoulos GL et al (2009) Lost in translation, an assessment and perspective for computational microRNA target identification. Bioinformatics 25:3049–3055

    Article  PubMed  CAS  Google Scholar 

  8. Barbato C, Arisi I, Frizzo ME et al (2009) Computational challenges in miRNATarget predictions, to be or not to be a true target? J Biomed Biotechnol 2009:803069

    PubMed  Google Scholar 

  9. Krek A, Grun D, Poy MN et al (2005) Combinatorial microRNA target predictions. Nat Genet 37:495–500

    Article  PubMed  CAS  Google Scholar 

  10. Wang J, Lu M, Qiu C et al (2010) TransmiR, a transcription factor–microRNA regulation database. Nucleic Acids Res 38:D119–D122

    Article  PubMed  CAS  Google Scholar 

  11. Heckerman D (1998) Tutorial on learning with Bayesian networks. In: Jordan M (ed) Learning in graphical models. Adaptive computation and machine learning. MIT Press, Massachusetts, pp 301–354

    Chapter  Google Scholar 

  12. Segal E, Shapira M, Regev A et al (2005) Learning module networks. J Mach Learn Res 6:557–588

    Google Scholar 

  13. Tsang J, Zhu J, van Oudenaarden A (2007) MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals. Mol Cell 26:753–767

    Article  PubMed  CAS  Google Scholar 

  14. Liu N, Olson EN (2010) MicroRNA regulatory networks in cardiovascular development. Dev Cell 18:510–525

    Article  PubMed  CAS  Google Scholar 

  15. El Gazzar M, McCall CE (2011) MicroRNAs regulatory networks in myeloid lineage development and differentiation, regulators of the regulators. Immunol Cell Biol. doi:10.1038/icb.2011.74

    PubMed  Google Scholar 

  16. Stingo FC, Chen YA, Vannucci M et al (2010) A Bayesian graphical modeling approach to microRNA regulatory network inference. Ann Appl Stat 4:2024–2048

    Article  PubMed  Google Scholar 

  17. Tang J, Fang J (2009) MicroRNA regulatory network in human colorectal cancer. Mini Rev Med Chem 9:921–926

    Article  PubMed  CAS  Google Scholar 

  18. Liu B, Li J, Tsykin A et al (2009) Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy. BMC Bioinformatics 10:408

    Article  PubMed  Google Scholar 

  19. Vasudevan S, Tong Y, Steitz JA (2007) Switching from repression to activation: MicroRNAs can up-regulate translation. Science 318:1931–1934

    Article  PubMed  CAS  Google Scholar 

  20. Bonnet E, Michoel T, Van de Peer Y (2010) Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data. Bioinformatics 26:i638–i644

    Article  PubMed  CAS  Google Scholar 

  21. Segal E, Shapira M, Regev A et al (2003) Module networks, discovering regulatory modules and their condition specific regulators from gene expression data. Nat Genet 34:166–176

    Article  PubMed  CAS  Google Scholar 

  22. Michoel T, Maere S, Bonnet E et al (2007) Validating module networks learning algorithms using simulated data. BMC Bioinformatics 8:S5

    Article  PubMed  Google Scholar 

  23. Yoon S, Micheli G (2005) Prediction of regulatory modules comprising microRNAs and target genes. Bioinformatics 21:i93–i100

    Article  Google Scholar 

  24. Joung JG, Hwang KB, Nam JW et al (2007) Discovery of microRNA-mRNA modules via population-based probabilistic learning. Bioinformatics 23:1141–1147

    Article  PubMed  CAS  Google Scholar 

  25. Lu J, Getz G, Miska EA et al (2005) MicroRNA expression profiles classify human cancers. Nature 435:834–838

    Article  PubMed  CAS  Google Scholar 

  26. Liu B, Li J, Tsykin A (2009) Discovery of functional miRNA-mRNA regulatory modules with computational methods. J Biomed Inform 42:685–691

    Article  PubMed  CAS  Google Scholar 

  27. Peng X, Li Y, Walters KA et al (2009) Computational identification of hepatitis c virus associated microRNA-mRNA regulatory modules in human livers. BMC Genomics 10:373

    Article  PubMed  Google Scholar 

  28. Madeira SC, Oliveira AL (2004) Biclustering algorithms for biological data analysis, a survey. IEEE/ACM Trans Comput Biol Bioinform 1:24–45

    Article  PubMed  CAS  Google Scholar 

  29. Cheng Y, Church GM (2000) Biclustering of expression data. Proc 8th int conf intel syst mol biol, pp 93–103

    Google Scholar 

  30. Getz G, Levine E, Domany E (2000) Coupled two-way clustering analysis of gene microarray data. Proc Natl Acad Sci U S A 97:12079–12084

    Article  PubMed  CAS  Google Scholar 

  31. Yang J, Wang W, Wang H (2003) Enhanced biclustering on expression data. Proc 3rd IEEE conf bioinform bioeng, pp 321–327

    Google Scholar 

  32. Tang C, Zhang L, Zhang I et al (2001) Interrelated two-way clustering, an unsupervised approach for gene expression data analysis. Proc 2nd IEEE int sym bioinform bioeng, pp 41–48, 2001

    Google Scholar 

  33. Hartigan JA (1972) Direct clustering of a data matrix. J Am Stat Assoc 67:123–129

    Article  Google Scholar 

  34. Caldas J, Kaski S (2011) Hierarchical generative biclustering for microRNA expression analysis. J Comput Biol 18:251–261

    Article  PubMed  CAS  Google Scholar 

  35. Nam S, Li M, Choi K et al (2009) MicroRNA and mRNA integrated analysis (MMIA), a web tool for examining biological functions of microRNA expression. Nucleic Acids Res 37:W356–W362

    Article  PubMed  CAS  Google Scholar 

  36. Ulitsky I, Laurent LC, Shamir R (2010) Towards computational prediction of microRNA function and activity. Nucleic Acids Res. doi:10.1093/nar/gkq570

    Google Scholar 

  37. Sales G, Coppe A, Bisognin A et al (2010) MAGIA, a web-based tool for miRNA and genes integrated analysis. Nucleic Acids Res 38:W352–W359

    Article  PubMed  CAS  Google Scholar 

  38. Wu X, Watson M (2009) CORNA, testing gene lists for regulation by microRNAs. Bioinformatics 25:832–833

    Article  PubMed  CAS  Google Scholar 

  39. Huopaniemi I, Suvitaival T, Nikkilä J et al (2010) Multivariate multi-way analysis of multi-source data. Bioinformatics 26:i391–i398

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgement

This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under the Project 110E160.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Baskent University, Ankara, Turkey

    Hasan Oğul

Authors
  1. Hasan Oğul
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dabburiya Village, Israel

    Malik Yousef

  2. Izmir Institute of Technology, Izmir, Turkey

    Jens Allmer

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this protocol

Cite this protocol

Oğul, H. (2014). Computational Prediction of MicroRNA Function and Activity. In: Yousef, M., Allmer, J. (eds) miRNomics: MicroRNA Biology and Computational Analysis. Methods in Molecular Biology, vol 1107. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-748-8_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-62703-748-8_15

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-747-1

  • Online ISBN: 978-1-62703-748-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation