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Computational Methods for MicroRNA Target Prediction

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miRNomics: MicroRNA Biology and Computational Analysis

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

Abstract

MicroRNAs (miRNAs) are important players in gene regulation. The final and maybe the most important step in their regulatory pathway is the targeting. Targeting is the binding of the miRNA to the mature RNA via the RNA-induced silencing complex. Expression patterns of miRNAs are highly specific in respect to external stimuli, developmental stage, or tissue. This is used to diagnose diseases such as cancer in which the expression levels of miRNAs are known to change considerably. Newly identified miRNAs are increasing in number with every new release of miRBase which is the main online database providing miRNA sequences and annotation. Many of these newly identified miRNAs do not yet have identified targets. This is especially the case in animals where the miRNA does not bind to its target as perfectly as it does in plants. Valid targets need to be identified for miRNAs in order to properly understand their role in cellular pathways. Experimental methods for target validations are difficult, expensive, and time consuming. Having considered all these facts it is of crucial importance to have accurate computational miRNA target predictions. There are many proposed methods and algorithms available for predicting targets for miRNAs, but only a few have been developed to become available as independent tools and software. There are also databases which collect and store information regarding predicted miRNA targets. Current approaches to miRNA target prediction produce a huge amount of false positive and an unknown amount of false negative results, and thus the need for better approaches is evermore evident. This chapter aims to give some detail about the current tools and approaches used for miRNA target prediction, provides some grounds for their comparison, and outlines a possible future.

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References

  1. Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39: D152–D157

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  3. Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:15–20

    Article  PubMed  CAS  Google Scholar 

  4. Wu S, Huang S, Ding J et al (2010) Multiple microRNAs modulate p21Cip1/Waf1 expression by directly targeting its 3′ untranslated region. Oncogene 29:2302–2308

    Article  PubMed  CAS  Google Scholar 

  5. Vergoulis T, Vlachos IS, Alexiou P et al (2012) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40:D222–D229

    Article  PubMed  CAS  Google Scholar 

  6. Miranda KC, Huynh T, Tay Y et al (2006) A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes. Cell 126:1203–1217

    Article  PubMed  CAS  Google Scholar 

  7. Friedman RC, Farh KK-H, Burge CB et al (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 19:92–105

    Article  PubMed  CAS  Google Scholar 

  8. Xie Z, Kasschau KD, Carrington JC (2003) Negative feedback regulation of Dicer-Like1 in Arabidopsis by microRNA-guided mRNA degradation. Curr Biol 13:784–789

    Article  PubMed  CAS  Google Scholar 

  9. Johnson CD, Esquela-Kerscher A, Stefani G et al (2007) The let-7 microRNA represses cell proliferation pathways in human cells. Cancer Res 67:7713–7722

    Article  PubMed  CAS  Google Scholar 

  10. Tokumaru S, Suzuki M, Yamada H et al (2008) let-7 regulates Dicer expression and constitutes a negative feedback loop. Carcinogenesis 29: 2073–2077

    Article  PubMed  CAS  Google Scholar 

  11. Mazière P, Enright AJ (2007) Prediction of microRNA targets. Drug Discov Today 12: 452–458

    Article  PubMed  Google Scholar 

  12. Place RF, Li L-C, Pookot D et al (2008) MicroRNA-373 induces expression of genes with complementary promoter sequences. Proc Natl Acad Sci U S A 105:1608–1613

    Article  PubMed  CAS  Google Scholar 

  13. Tay Y, Zhang J, Thomson AM et al (2008) MicroRNAs to Nanog, Oct4 and Sox2 coding regions modulate embryonic stem cell differentiation. Nature 455:1124–1128

    Article  PubMed  CAS  Google Scholar 

  14. Ørom UA, Nielsen FC, Lund AH (2008) MicroRNA-10a binds the 5′UTR of ribosomal protein mRNAs and enhances their translation. Mol Cell 30:460–471

    Article  PubMed  Google Scholar 

  15. Forman JJ, Legesse-Miller A, Coller HA (2008) A search for conserved sequences in coding regions reveals that the let-7 microRNA targets Dicer within its coding sequence. Proc Natl Acad Sci U S A 105:14879–14884

    Article  PubMed  CAS  Google Scholar 

  16. Reczko M, Maragkakis M, Alexiou P et al (2012) Functional microRNA targets in protein coding sequences. Bioinformatics 28:771–776

    Article  PubMed  CAS  Google Scholar 

  17. Jones-Rhoades MW, Bartel DP (2004) Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell 14:787–799

    Article  PubMed  CAS  Google Scholar 

  18. Selbach M, Schwanhäusser B, Thierfelder N et al (2008) Widespread changes in protein synthesis induced by microRNAs. Nature 455: 58–63

    Article  PubMed  CAS  Google Scholar 

  19. Lytle JR, Yario TA, Steitz JA (2007) Target mRNAs are repressed as efficiently by microRNA-binding sites in the 5′ UTR as in the 3′ UTR. Proc Natl Acad Sci U S A 104: 9667–9672

    Article  PubMed  CAS  Google Scholar 

  20. Gu S, Jin L, Zhang F et al (2009) Biological basis for restriction of microRNA targets to the 3′ untranslated region in mammalian mRNAs. Nat Struct Mol Biol 16:144–150

    Article  PubMed  CAS  Google Scholar 

  21. Vaucheret H (2006) Post-transcriptional small RNA pathways in plants: mechanisms and regulations. Genes Dev 20:759–771

    Article  PubMed  CAS  Google Scholar 

  22. Rhoades MW, Reinhart BJ, Lim LP et al (2002) Prediction of plant microRNA targets. Cell 110:513–520

    Article  PubMed  CAS  Google Scholar 

  23. Zhang Y (2005) miRU: an automated plant miRNA target prediction server. Nucleic Acids Res 33:W701–W704

    Article  PubMed  CAS  Google Scholar 

  24. Lewis BP, Shih I, Jones-Rhoades MW et al (2003) Prediction of mammalian microRNA targets. Cell 115:787–798

    Article  PubMed  CAS  Google Scholar 

  25. Liu J (2008) Control of protein synthesis and mRNA degradation by microRNAs. Curr Opin Cell Biol 20:214–221

    Article  PubMed  CAS  Google Scholar 

  26. Grimson A, Farh KK-H, Johnston WK et al (2007) MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 27:91–105

    Article  PubMed  CAS  Google Scholar 

  27. Sethupathy P, Megraw M, Hatzigeorgiou AG (2006) A guide through present computational approaches for the identification of mammalian microRNA targets. Nat Methods 3:881–886

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  29. Doench JG, Sharp PA (2004) Specificity of microRNA target selection in translational repression. Genes Dev 18:504–511

    Article  PubMed  CAS  Google Scholar 

  30. Lai EC (2002) Micro RNAs are complementary to 3′ UTR sequence motifs that mediate negative post-transcriptional regulation. Nat Genet 30:363–364

    Article  PubMed  CAS  Google Scholar 

  31. Rajewsky N, Socci ND (2004) Computational identification of microRNA targets. Dev Biol 267:529–535

    Article  PubMed  CAS  Google Scholar 

  32. Du T, Zamore PD (2005) microPrimer: the biogenesis and function of microRNA. Development 132:4645–4652

    Article  PubMed  CAS  Google Scholar 

  33. Brennecke J, Stark A, Russell RB et al (2005) Principles of microRNA-target recognition. PLoS Biol 3:e85

    Article  PubMed  Google Scholar 

  34. Yan X, Chao T, Tu K et al (2007) Improving the prediction of human microRNA target genes by using ensemble algorithm. FEBS Lett 581:1587–1593

    Article  PubMed  CAS  Google Scholar 

  35. Sethupathy P, Corda B, Hatzigeorgiou AG (2006) TarBase: a comprehensive database of experimentally supported animal microRNA targets. RNA 12:192–197

    Article  PubMed  CAS  Google Scholar 

  36. Hubbard T (2002) The Ensembl genome database project. Nucleic Acids Res 30:38–41

    Article  PubMed  CAS  Google Scholar 

  37. Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31:3406–3415

    Article  PubMed  CAS  Google Scholar 

  38. Hofacker IL (2003) Vienna RNA secondary structure server. Nucleic Acids Res 31: 3429–3431

    Article  PubMed  CAS  Google Scholar 

  39. Enright AJ, John B, Gaul U et al (2003) MicroRNA targets in Drosophila. Genome Biol 5:R1

    Article  PubMed  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  41. Krüger J, Rehmsmeier M (2006) RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res 34:W451–W454

    Article  PubMed  Google Scholar 

  42. Stark A, Brennecke J, Russell RB et al (2003) Identification of Drosophila microRNA targets. PLoS Biol 1:E60

    Article  PubMed  Google Scholar 

  43. John B, Enright AJ, Aravin A et al (2004) Human microRNA targets. PLoS Biol 2:e363

    Article  PubMed  Google Scholar 

  44. Saetrom O, Snøve O, Saetrom P (2005) Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms. RNA 11:995–1003

    Article  PubMed  CAS  Google Scholar 

  45. Kim S-K, Nam J-W, Lee W-J et al (2005) A Kernel method for microRNA target prediction using sensible data and position-based features. 2005 IEEE symposium on computational intelligence in bioinformatics and computational biology, IEEE, pp 1–7

    Google Scholar 

  46. Yousef M, Jung S, Kossenkov AV et al (2007) Naïve Bayes for microRNA target predictions—machine learning for microRNA targets. Bioinformatics 23:2987–2992

    Article  PubMed  CAS  Google Scholar 

  47. Thadani R, Tammi MT (2006) MicroTar: predicting microRNA targets from RNA duplexes. BMC Bioinformatics 7(Suppl 5):S20

    Article  PubMed  Google Scholar 

  48. Rehmsmeier M, Steffen P, Hochsmann M et al (2004) Fast and effective prediction of microRNA/target duplexes. RNA 10:1507–1517

    Article  PubMed  CAS  Google Scholar 

  49. Stark A, Brennecke J, Bushati N et al (2005) Animal microRNAs confer robustness to gene expression and have a significant impact on 3′UTR evolution. Cell 123:1133–1146

    Article  PubMed  CAS  Google Scholar 

  50. Kertesz M, Iovino N, Unnerstall U et al (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39:1278–1284

    Article  PubMed  CAS  Google Scholar 

  51. Gaidatzis D, van Nimwegen E, Hausser J et al (2007) Inference of miRNA targets using evolutionary conservation and pathway analysis. BMC Bioinformatics 8:69

    Article  PubMed  Google Scholar 

  52. Hammell M, Long D, Zhang L et al (2008) mirWIP: microRNA target prediction based on microRNA-containing ribonucleoprotein-enriched transcripts. Nat Methods 5:813–819

    Article  PubMed  CAS  Google Scholar 

  53. Nam S, Kim B, Shin S et al (2008) miRGator: an integrated system for functional annotation of microRNAs. Nucleic Acids Res 36: D159–D164

    Article  PubMed  CAS  Google Scholar 

  54. Creighton CJ, Nagaraja AK, Hanash SM et al (2008) A bioinformatics tool for linking gene expression profiling results with public databases of microRNA target predictions. RNA 14:2290–2296

    Article  PubMed  CAS  Google Scholar 

  55. Yang Y, Wang Y-P, Li K-B (2008) MiRTif: a support vector machine-based microRNA target interaction filter. BMC Bioinformatics 9(Suppl 12):S4

    Article  PubMed  Google Scholar 

  56. Lin S, Ding J (2009) Integration of ranked lists via cross entropy Monte Carlo with applications to mRNA and microRNA Studies. Biometrics 65:9–18

    Article  PubMed  CAS  Google Scholar 

  57. Joung J-G, Fei Z (2009) Computational identification of condition-specific miRNA targets based on gene expression profiles and sequence information. BMC Bioinformatics 10(Suppl 1):S34

    Article  PubMed  Google Scholar 

  58. Huang JC, Morris QD, Frey BJ (2007) Bayesian inference of MicroRNA targets from sequence and expression data. J Comput Biol 14:550–563

    Article  PubMed  CAS  Google Scholar 

  59. Cheng C, Li LM (2008) Inferring microRNA activities by combining gene expression with microRNA target prediction. PLoS One 3:e1989

    Article  PubMed  Google Scholar 

  60. Dai X, Zhao PX (2011) psRNATarget: a plant small RNA target analysis server. Nucleic Acids Res 39:W155–W159

    Article  PubMed  CAS  Google Scholar 

  61. Xiao F, Zuo Z, Cai G et al (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 37:D105–D110

    Article  PubMed  CAS  Google Scholar 

  62. Dweep H, Sticht C, Pandey P et al (2011) miRWalk-database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform 44: 839–847

    Article  PubMed  CAS  Google Scholar 

  63. Kim S-K, Nam J-W, Rhee J-K et al (2006) miTarget: microRNA target gene prediction using a support vector machine. BMC Bioinformatics 7:411

    Article  PubMed  Google Scholar 

  64. Wang X (2008) miRDB: a microRNA target prediction and functional annotation database with a wiki interface. RNA 14:1012–1017

    Article  PubMed  CAS  Google Scholar 

  65. Jiang Q, Feng M-G, Mo Y-Y (2009) Systematic validation of predicted microRNAs for cyclin D1. BMC Cancer 9:194

    Article  PubMed  Google Scholar 

  66. Saetrom P, Heale BSE, Snøve O et al (2007) Distance constraints between microRNA target sites dictate efficacy and cooperativity. Nucleic Acids Res 35:2333–2342

    Article  PubMed  CAS  Google Scholar 

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

    PubMed  Google Scholar 

  68. Kloosterman WP, Wienholds E, Ketting RF et al (2004) Substrate requirements for let-7 function in the developing zebrafish embryo. Nucleic Acids Res 32:6284–6291

    Article  PubMed  CAS  Google Scholar 

  69. Peter ME (2010) Targeting of mRNAs by multiple miRNAs: the next step. Oncogene 29: 2161–2164

    Article  PubMed  CAS  Google Scholar 

  70. Mathews DH, Sabina J, Zuker M et al (1999) Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J Mol Biol 288:911–940

    Article  PubMed  CAS  Google Scholar 

  71. Hofacker I, Fontana W, Stadler P et al (1994) Fast folding and comparison of RNA secondary structures. Monatsh Chem 125:167–188

    Article  CAS  Google Scholar 

  72. Hsu S-D, Lin F-M, Wu W-Y et al (2011) miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 39:D163–D169

    Article  PubMed  CAS  Google Scholar 

  73. Shahi P, Loukianiouk S, Bohne-Lang A et al (2006) Argonaute—a database for gene regulation by mammalian microRNAs. Nucleic Acids Res 34:D115–D118

    Article  PubMed  CAS  Google Scholar 

  74. Darty K, Denise A, Ponty Y (2009) VARNA: interactive drawing and editing of the RNA secondary structure. Bioinformatics 25:1974–1975

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

H.H. would like to thank Associate Professor Dr. Jens Allmer for his kind guidance, encouragement, and advice and would also like to thank his parents for their ever-increasing support. JA would like to thank his wife Açalya for the tolerance towards late hours spent on this and other work in this volume and his son Lukas Aren for providing fun distractions during the process.

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Authors and Affiliations

  1. Molecular Biology and Genetics, Izmir Institute of Technology, Izmir, Turkey

    Hamid Hamzeiy & Jens Allmer

  2. The College of Sakhnin, Sakhnin, Israel

    Malik Yousef

  3. The Galilee Society Institute of Applied Research, Shefa Amr, Israel

    Malik Yousef

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  1. Hamid Hamzeiy
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  2. Jens Allmer
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  3. Malik Yousef
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Editors and Affiliations

  1. Dabburiya Village, Israel

    Malik Yousef

  2. Izmir Institute of Technology, Izmir, Turkey

    Jens Allmer

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Hamzeiy, H., Allmer, J., Yousef, M. (2014). Computational Methods for MicroRNA Target Prediction. 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_12

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  • DOI: https://doi.org/10.1007/978-1-62703-748-8_12

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-747-1

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