Advertisement

The Limitations of Existing Approaches in Improving MicroRNA Target Prediction Accuracy

  • Rasiah LoganantharajEmail author
  • Thomas A. Randall
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1617)

Abstract

MicroRNAs (miRNAs) are small (18–24 nt) endogenous RNAs found across diverse phyla involved in posttranscriptional regulation, primarily downregulation of mRNAs. Experimentally determining miRNA–mRNA interactions can be expensive and time-consuming, making the accurate computational prediction of miRNA targets a high priority. Since miRNA–mRNA base pairing in mammals is not perfectly complementary and only a fraction of the identified motifs are real binding sites, accurately predicting miRNA targets remains challenging. The limitations and bottlenecks of existing algorithms and approaches are discussed in this chapter.

A new miRNA–mRNA interaction algorithm was implemented in Python (TargetFind) to capture three different modes of association and to maximize detection sensitivity to around 95% for mouse (mm9) and human (hg19) reference data. For human (hg19) data, the prediction accuracy with any one feature among evolutionarily conserved score, multiple targets in a UTR or changes in free energy varied within a close range from 63.5% to 66%. When the results of these features are combined with majority voting, the expected prediction accuracy increases to 69.5%. When all three features are used together, the average best prediction accuracy with tenfold cross validation from the classifiers naïve Bayes, support vector machine, artificial neural network, and decision tree were, respectively, 66.5%, 67.1%, 69%, and 68.4%. The results reveal the advantages and limitations of these approaches.

When comparing different sets of features on their strength in predicting true hg19 targets, evolutionarily conserved score slightly outperformed all other features based on thermostability, and target multiplicity. The sophisticated supervised learning algorithms did not improve the prediction accuracy significantly compared to a simple threshold based approach on conservation score or combining the results of each feature with majority agreements. The targets from randomly generated UTRs behaved similar to that of noninteracting pairs with respect to changes in free energy. Availability of additional experimental data describing noninteracting pairs will advance our understanding of the characteristics and the factors positively and negatively influencing these interactions.

Key words

MicroRNA Transcript regulation Interacting miRNA–target genes Pattern matching Hybridization energy Thermostability UTR binding sites 

Abbreviations

ANN

Artificial neural network

PWM

Position weighted matrix

ROC

Receiver operating characteristic

SOM

Self-organizing map

SVM

Support vector machine

UTR

Untranslated region

Notes

Acknowledgments

Our sincere thanks to Hui Liu for sharing the noninteracting data set that they have collected on human genome.

References

  1. 1.
    Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ (2008) miRBase: tools for microRNA genomics. Nucleic Acids Res 36(Database issue):D154–D158PubMedGoogle Scholar
  2. 2.
    Vergoulis T, Vlachos IS, Alexiou P, Georgakilas G, Maragkakis M, Reczko M, Gerangelos S, Koziris N, Dalamagas T, Hatzigeorgiou AG (2012) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40(Database issue):D222–D229CrossRefPubMedGoogle Scholar
  3. 3.
    Sethupathy P, Corda B, Hatzigeorgiou AG (2006) TarBase: a comprehensive database of experimentally supported animal microRNA targets. RNA 12(2):192–197CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 37(Database issue):D105–D110CrossRefPubMedGoogle Scholar
  5. 5.
    Hsu SD, Lin FM, Wu WY, Liang C, Huang WC, Chan WL, Tsai WT, Chen GZ, Lee CJ, Chiu CM et al (2011) miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 39(Database issue):D163–D169CrossRefPubMedGoogle Scholar
  6. 6.
    Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, Li M, Wang G, Liu Y (2009) miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 37(Database issue):D98–104CrossRefPubMedGoogle Scholar
  7. 7.
    Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39(10):1278–1284CrossRefPubMedGoogle Scholar
  8. 8.
    Long D, Lee R, Williams P, Chan CY, Ambros V, Ding Y (2007) Potent effect of target structure on microRNA function. Nat Struct Mol Biol 14(4):287–294CrossRefPubMedGoogle Scholar
  9. 9.
    Heikkinen L, Kolehmainen M, Wong G (2011) Prediction of microRNA targets in Caenorhabditis elegans using a self-organizing map. Bioinformatics 27(9):1247–1254CrossRefPubMedGoogle Scholar
  10. 10.
    Liu H, Yue D, Chen Y, Gao SJ, Huang Y (2010) Improving performance of mammalian microRNA target prediction. BMC Bioinformatics 11:476CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS (2003) MicroRNA targets in drosophila. Genome Biol 5(1):R1CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Yousef M, Jung S, Kossenkov AV, Showe LC, Showe MK (2007) Naive Bayes for microRNA target predictions—machine learning for microRNA targets. Bioinformatics 23(22):2987–2992CrossRefPubMedGoogle Scholar
  13. 13.
    Mendoza MR, da Fonseca GC, Loss-Morais G, Alves R, Margis R, Bazzan AL (2013) RFMirTarget: predicting human MicroRNA target genes with a random Forest classifier. PLoS One 8(7):e70153CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M et al (2005) Combinatorial microRNA target predictions. Nat Genet 37(5):495–500CrossRefPubMedGoogle Scholar
  15. 15.
    Wang X, El Naqa IM (2008) Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics 24(3):325–332CrossRefPubMedGoogle Scholar
  16. 16.
    Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R (2004) Fast and effective prediction of microRNA/target duplexes. RNA 10(10):1507–1517CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120(1):15–20CrossRefPubMedGoogle Scholar
  18. 18.
    John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS (2004) Human MicroRNA targets. PLoS Biol 2(11):e363CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Nam S, Kim B, Shin S (2008) Lee S: miRGator: an integrated system for functional annotation of microRNAs. Nucleic Acids Res 36(Database issue):D159–D164PubMedGoogle Scholar
  20. 20.
    Vejnar CE, Zdobnov EM (2012) MiRmap: comprehensive prediction of microRNA target repression strength. Nucleic Acids Res 40(22):11673–11683CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Incarnato D, Neri F, Diamanti D, Oliviero S (2013) MREdictor: a two-step dynamic interaction model that accounts for mRNA accessibility and Pumilio binding accurately predicts microRNA targets. Nucleic Acids Res 41(18):8421–8433CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Chi SW, Zang JB, Mele A, Darnell RB (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460(7254):479–486PubMedPubMedCentralGoogle Scholar
  23. 23.
    Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136(2):215–233CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Brennecke J, Stark A, Russell RB, Cohen SM (2005) Principles of microRNA-target recognition. PLoS Biol 3(3):e85CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Lekprasert P, Mayhew M, Ohler U (2011) Assessing the utility of thermodynamic features for microRNA target prediction under relaxed seed and no conservation requirements. PLoS One 6(6):e20622CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Hofacker IL (2004) RNA secondary structure analysis using the Vienna RNA package. Curr Protoc Bioinformatics Chapter 12:Unit 12Google Scholar
  27. 27.
    Gruber AR, Lorenz R, Bernhart SH, Neubock R, Hofacker IL (2008) The Vienna RNA websuite. Nucleic Acids Res 36(Web Server issue):W70–W74CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Muckstein U, Tafer H, Hackermuller J, Bernhart SH, Stadler PF, Hofacker IL (2006) Thermodynamics of RNA-RNA binding. Bioinformatics 22(10):1177–1182CrossRefPubMedGoogle Scholar
  29. 29.
    Haider S, Ballester B, Smedley D, Zhang J, Rice P, Kasprzyk A (2009) BioMart central portal—unified access to biological data. Nucleic Acids Res 37:23–27CrossRefGoogle Scholar
  30. 30.
    bedtools (2012) In., 2.16.2 edn: http://code.google.com/p/bedtools/
  31. 31.
    Ivanciuc O (2008) Weka machine learning for predicting the phospholipidosis inducing potential. Curr Top Med Chem 8(18):1691–1709CrossRefPubMedGoogle Scholar
  32. 32.
    Frank E, Hall M, Trigg L, Holmes G, Witten IH (2004) Data mining in bioinformatics using Weka. Bioinformatics 20(15):2479–2481CrossRefPubMedGoogle Scholar
  33. 33.
    Pereira F, Mitchell T, Botvinick M (2009) Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 45(1 Suppl):S199–S209CrossRefPubMedGoogle Scholar
  34. 34.
    Baldi P, Brunak S (2001) Bioinformatics: the machine learning approach, 2nd edn. MIT Press, Cambridge, MAGoogle Scholar
  35. 35.
    Mitchell TM (1997) Machine learning. McGraw-Hill, New York, NYGoogle Scholar
  36. 36.
    Kent WJ (2002) BLAT—the BLAST-like alignment tool. Genome Res 12(4):656–664CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Friedman Y, Naamati G, Linial M (2010) MiRror: a combinatorial analysis web tool for ensembles of microRNAs and their targets. Bioinformatics 26(15):1920–1921CrossRefPubMedGoogle Scholar
  38. 38.
    Baek D, Villen J, Shin C, Camargo FD, Gygi SP, Bartel DP (2008) The impact of microRNAs on protein output. Nature 455(7209):64–71CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Selbach M, Schwanhausser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N (2008) Widespread changes in protein synthesis induced by microRNAs. Nature 455(7209):58–63CrossRefPubMedGoogle Scholar
  40. 40.
    Erhard F, Dolken L, Jaskiewicz L, Zimmer R (2013) PARma: identification of microRNA target sites in Argonaute PAR-CLIP data. Genome Biol 14(7):R79CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    van Iterson M, Bervoets S, de Meijer EJ, Buermans HP, Hoen PA, Menezes RX, Boer JM (2013) Integrated analysis of microRNA and mRNA expression: adding biological significance to microRNA target predictions. Nucleic Acids Res 41(15):e146CrossRefPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Bioinformatics Research Lab, The Center for Advanced Computer StudiesUniversity of LouisianaLafayetteUSA
  2. 2.Integrative BioinformaticsNational Institute of Environmental Health Sciences, National Institutes of HealthDurhamUSA

Personalised recommendations