Improving miRNA Target Prediction Using CLASH Data

  • Xiaoman Li
  • Haiyan HuEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1970)


In this chapter, we present a computational method, TarPmiR, for miRNA target prediction. TarPmiR is based on emerging features of miRNA–target interactions learned from CLASH (crosslinking, ligation and sequencing of hybrids) data. First, we introduce miRNA target prediction, delineate existing methods for miRNA target prediction, and discuss their usage and limitations. Next, we describe available CLASH data, the learning of new miRNA binding features from CLASH data, and the usage of CLASH features in miRNA target prediction. Finally, we detail the computational pipeline of TarPmiR, discuss its performance compared with existing computational methods for miRNA target prediction, and present its installation and usage for miRNA target prediction. This chapter will facilitate the common understanding of CLASH data, new characteristics of miRNA–target interactions, and the use of the CLASH based miRNA target prediction tool TarPmiR.

Key words

miRNA CLASH data miRNA target prediction New features TarPmiR 


  1. 1.
    Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281–297CrossRefGoogle Scholar
  2. 2.
    Agarwal V, Bell GW, Nam JW et al (2015) Predicting effective microRNA target sites in mammalian mRNAs. eLife 4Google Scholar
  3. 3.
    Kuhn DE, Martin MM, Feldman DS et al (2008) Experimental validation of miRNA targets. Methods 44:47–54CrossRefGoogle Scholar
  4. 4.
    Thomson DW, Bracken CP, Goodall GJ (2011) Experimental strategies for microRNA target identification. Nucleic Acids Res 39:6845–6853CrossRefGoogle Scholar
  5. 5.
    Hafner M, Landthaler M, Burger L et al (2010) Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141:129–141CrossRefGoogle Scholar
  6. 6.
    Helwak A, Kudla G, Dudnakova T et al (2013) Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153:654–665CrossRefGoogle Scholar
  7. 7.
    Moore MJ, Scheel TK, Luna JM et al (2015) miRNA-target chimeras reveal miRNA 3′-end pairing as a major determinant of Argonaute target specificity. Nat Commun 6:8864CrossRefGoogle Scholar
  8. 8.
    Chi SW, Zang JB, Mele A et al (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460:479–486CrossRefGoogle Scholar
  9. 9.
    Ding J, Li X, Hu H (2016) TarPmiR: a new approach for microRNA target site prediction. Bioinformatics 32(18):2768–2775CrossRefGoogle Scholar
  10. 10.
    Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:15–20CrossRefGoogle Scholar
  11. 11.
    Lewis BP, Shih I-H, Jones-Rhoades MW et al (2003) Prediction of mammalian microRNA targets. Cell 115:787–798CrossRefGoogle Scholar
  12. 12.
    Ule J, Jensen KB, Ruggiu M et al (2003) CLIP identifies Nova-regulated RNA networks in the brain. Science 302:1212–1215CrossRefGoogle Scholar
  13. 13.
    Vejnar CE, Zdobnov EM (2012) MiRmap: comprehensive prediction of microRNA target repression strength. Nucleic Acids Res 40:11673–11683CrossRefGoogle Scholar
  14. 14.
    Enright AJ, John B, Gaul U et al (2004) MicroRNA targets in drosophila. Genome Biol 5:R1–R1CrossRefGoogle Scholar
  15. 15.
    Paraskevopoulou MD, Georgakilas G, Kostoulas N et al (2013) DIANA-microT web server v5. 0: service integration into miRNA functional analysis workflows. Nucleic Acids Res 41(Web Server issue):W169–W173CrossRefGoogle Scholar
  16. 16.
    Loher P, Rigoutsos I (2012) Interactive exploration of RNA22 microRNA target predictions. Bioinformatics 28:3322–3323CrossRefGoogle Scholar
  17. 17.
    Friedman RC, Farh KK-H, Burge CB et al (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 19:92–105CrossRefGoogle Scholar
  18. 18.
    Wang X (2010) Computational prediction of microRNA targets. Methods Mol Biol 667:283–295CrossRefGoogle Scholar
  19. 19.
    Ding J, Li X, Hu H (2014) MicroRNA modules prefer to bind weak and unconventional target sites. Bioinformatics. btu833Google Scholar
  20. 20.
    Wang XW (2014) Composition of seed sequence is a major determinant of microRNA targeting patterns. Bioinformatics 30:1377–1383CrossRefGoogle Scholar
  21. 21.
    Kishore S, Jaskiewicz L, Burger L et al (2011) A quantitative analysis of CLIP methods for identifying binding sites of RNA-binding proteins. Nat Methods 8:559–564CrossRefGoogle Scholar
  22. 22.
    Vlachos IS, Paraskevopoulou MD, Karagkouni D et al (2015) DIANA-TarBase v7. 0: indexing more than half a million experimentally supported miRNA: mRNA interactions. Nucleic Acids Res 43:D153–D159CrossRefGoogle Scholar
  23. 23.
    Wang Y, Li X, Hu H (2011) Transcriptional regulation of co-expressed microRNA target genes. Genomics 98:445–452CrossRefGoogle Scholar
  24. 24.
    Ding J, Li X, Hu H (2017) CCmiR: a computational approach for competitive and cooperative microRNA binding prediction. BioinformaticsGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Burnett School of Biomedical Science, College of MedicineUniversity of Central FloridaOrlandoUSA
  2. 2.Department of Computer ScienceUniversity of Central FloridaOrlandoUSA

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