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A Practical Guide to miRNA Target Prediction

  • Most Mauluda Akhtar
  • Luigina Micolucci
  • Md Soriful Islam
  • Fabiola Olivieri
  • Antonio Domenico Procopio
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1970)

Abstract

MicroRNAs (miRNAs) are small endogenous noncoding RNA molecules that posttranscriptionally regulate gene expression. Since their discovery, a huge number of miRNAs have been identified in a wide range of species. Through binding to the 3′ UTR of mRNA, miRNA can block translation or stimulate degradation of the targeted mRNA, thus affecting nearly all biological processes. Prediction and identification of miRNA target genes is crucial toward understanding the biology of miRNAs. Currently, a number of sophisticated bioinformatics approaches are available to perform effective prediction of miRNA target sites. In this chapter, we present the major features that most algorithms take into account to efficiently predict miRNA target: seed match, free energy, conservation, target site accessibility, and contribution of multiple binding sites. We also give an overview of the frequently used bioinformatics tools for miRNA target prediction. Understanding the basis of these prediction methodologies may help users to better select the appropriate tools and analyze their output.

Key words

MicroRNA Target prediction Seed match Conservation Free energy Site accessibility Bioinformatic tools 

References

  1. 1.
    Lee RC, Feinbaum RL, Ambros V (1993) The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75(5):843–854CrossRefGoogle Scholar
  2. 2.
    Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G (2000) The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 403(6772):901–906PubMedCrossRefGoogle Scholar
  3. 3.
    Kozomara A, Griffiths-Jones S (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42(D1):D68–D73PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Winter J, Jung S, Keller S, Gregory RI, Diederichs S (2009) Many roads to maturity: microRNA biogenesis pathways and their regulation. Nat Cell Biol 11(3):228–234PubMedCrossRefGoogle Scholar
  5. 5.
    Friedman RC, Farh KK-H, Burge CB, Bartel DP (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 19(1):92–105PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116(2):281–297CrossRefGoogle Scholar
  7. 7.
    Thomson DW, Bracken CP, Goodall GJ (2011) Experimental strategies for microRNA target identification. Nucleic Acids Res 39(16):6845–6853PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    Zhao Y, Samal E, Srivastava D (2005) Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis. Nature 436(7048):214PubMedCrossRefGoogle Scholar
  9. 9.
    Cheng AM, Byrom MW, Shelton J, Ford LP (2005) Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Res 33(4):1290–1297PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Hatfield S, Shcherbata H, Fischer K, Nakahara K, Carthew R, Ruohola-Baker H (2005) Stem cell division is regulated by the microRNA pathway. Nature 435(7044):974PubMedCrossRefGoogle Scholar
  11. 11.
    Naguibneva I, Ameyar-Zazoua M, Polesskaya A, Ait-Si-Ali S, Groisman R, Souidi M, Cuvellier S, Harel-Bellan A (2006) The microRNA miR-181 targets the homeobox protein Hox-A11 during mammalian myoblast differentiation. Nat Cell Biol 8(3):278PubMedCrossRefGoogle Scholar
  12. 12.
    Novák J, Olejníčková V, Tkáčová N, Santulli G (2015) Mechanistic role of microRNAs in coupling lipid metabolism and atherosclerosis. In: MicroRNA: basic science. Springer, New York, pp 79–100CrossRefGoogle Scholar
  13. 13.
    Cho W (2010) MicroRNAs: potential biomarkers for cancer diagnosis, prognosis and targets for therapy. Int J Biochem Cell Biol 42(8):1273–1281PubMedCrossRefGoogle Scholar
  14. 14.
    Micolucci L, Akhtar MM, Olivieri F, Rippo MR, Procopio AD (2016) Diagnostic value of microRNAs in asbestos exposure and malignant mesothelioma: systematic review and qualitative meta-analysis. Oncotarget 7(36):58606PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Olivieri F, Capri M, Bonafè M, Morsiani C, Jung HJ, Spazzafumo L, Viña J, Suh Y (2017) Circulating miRNAs and miRNA shuttles as biomarkers: perspective trajectories of healthy and unhealthy aging. Mech Ageing Dev 165:162–170PubMedCrossRefGoogle Scholar
  16. 16.
    Akhtar MM, Micolucci L, Islam MS, Olivieri F, Procopio AD (2015) Bioinformatic tools for microRNA dissection. Nucleic Acids Res 44(1):24–44PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Zhang Y (2005) miRU: an automated plant miRNA target prediction server. Nucleic Acids Res 33(suppl 2):W701–W704PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Ambros V (2004) The functions of animal microRNAs. Nature 431(7006):350–355PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136(2):215–233PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Peterson SM, Thompson JA, Ufkin ML, Sathyanarayana P, Liaw L, Congdon CB (2014) Common features of microRNA target prediction tools. Front Genet 5:23PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    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):15–20PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Wuchty S, Fontana W, Hofacker IL, Schuster P (1999) Complete suboptimal folding of RNA and the stability of secondary structures. Biopolymers 49(2):145–165PubMedCrossRefGoogle Scholar
  23. 23.
    Lewis BP, Shih I, Jones-Rhoades MW, Bartel DP, Burge CB (2003) Prediction of mammalian microRNA targets. Cell 115(7):787–798PubMedCrossRefGoogle Scholar
  24. 24.
    Lai EC (2004) Predicting and validating microRNA targets. Genome Biol 5(9):115PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Waterman MS, Eggert M (1987) A new algorithm for best subsequence alignments with application to tRNA-rRNA comparisons. J Mol Biol 197(4):723–728PubMedCrossRefGoogle Scholar
  26. 26.
    Bray N, Dubchak I, Pachter L (2003) AVID: a global alignment program. Genome Res 13(1):97–102PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Couronne O, Poliakov A, Bray N, Ishkhanov T, Ryaboy D, Rubin E, Pachter L, Dubchak I (2003) Strategies and tools for whole-genome alignments. Genome Res 13(1):73–80PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Rosenbloom KR, Armstrong J, Barber GP, Casper J, Clawson H, Diekhans M, Dreszer TR, Fujita PA, Guruvadoo L, Haeussler M (2014) The UCSC genome browser database: 2015 update. Nucleic Acids Res 43(D1):D670–D681PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Robins H, Li Y, Padgett RW (2005) Incorporating structure to predict microRNA targets. Proc Natl Acad Sci U S A 102(11):4006–4009PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    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):287PubMedCrossRefGoogle Scholar
  31. 31.
    Marín RM, Vaníček J (2010) Efficient use of accessibility in microRNA target prediction. Nucleic Acids Res 39(1):19–29PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS (2003) MicroRNA targets in Drosophila. Genome Biol 5(1):R1PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Watanabe Y, Yachie N, Numata K, Saito R, Kanai A, Tomita M (2006) Computational analysis of microRNA targets in Caenorhabditis elegans. Gene 365:2–10PubMedCrossRefGoogle Scholar
  34. 34.
    Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, Da Piedade I, Gunsalus KC, Stoffel M (2005) Combinatorial microRNA target predictions. Nat Genet 37(5):495PubMedCrossRefGoogle Scholar
  35. 35.
    Ritchie W, Rasko JE, Flamant S (2013) MicroRNA target prediction and validation. In: MicroRNA cancer regulation. Springer, New York, pp 39–53CrossRefGoogle Scholar
  36. 36.
    Betel D, Koppal A, Agius P, Sander C, Leslie C (2010) Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol 11(8):R90PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Betel D, Wilson M, Gabow A, Marks DS, Sander C (2008) The microRNA. org resource: targets and expression. Nucleic Acids Res 36(suppl 1):D149–D153PubMedGoogle Scholar
  38. 38.
    Agarwal V, Bell GW, Nam J-W, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. eLife 4Google Scholar
  39. 39.
    Rehmsmeier M, Steffen P, Höchsmann M, Giegerich R (2004) Fast and effective prediction of microRNA/target duplexes. RNA 10(10):1507–1517PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Krüger J, Rehmsmeier M (2006) RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res 34(suppl 2):W451–W454PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Lall S, Grün D, Krek A, Chen K, Wang Y-L, Dewey CN, Sood P, Colombo T, Bray N, MacMenamin P (2006) A genome-wide map of conserved MicroRNA targets in C. elegans. Curr Biol 16(5):460–471PubMedCrossRefGoogle Scholar
  42. 42.
    Miranda KC, Huynh T, Tay Y, Ang Y-S, Tam W-L, Thomson AM, Lim B, Rigoutsos I (2006) A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 126(6):1203–1217PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Loher P, Rigoutsos I (2012) Interactive exploration of RNA22 microRNA target predictions. Bioinformatics 28(24):3322–3323PubMedCrossRefGoogle Scholar
  44. 44.
    Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39(10):1278–1284PubMedCrossRefGoogle Scholar
  45. 45.
    Wang X (2008) miRDB: a microRNA target prediction and functional annotation database with a wiki interface. RNA 14(6):1012–1017PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Wong N, Wang X (2014) miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res 43(D1):D146–D152PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M, Filippidis C, Dalamagas T, Hatzigeorgiou AG (2013) DIANA-microT web server v5. 0: service integration into miRNA functional analysis workflows. Nucleic Acids Res 41(W1):W169–W173PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Rennie W, Liu C, Carmack CS, Wolenc A, Kanoria S, Lu J, Long D, Ding Y (2014) STarMir: a web server for prediction of microRNA binding sites. Nucleic Acids Res 42(Web Server issue):W114–W118. (In press):gku376PubMedPubMedCentralCrossRefGoogle Scholar
  49. 49.
    John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS (2004) Human microRNA targets. PLoS Biol 2(11):e363PubMedPubMedCentralCrossRefGoogle Scholar
  50. 50.
    Maragkakis M, Reczko M, Simossis VA, Alexiou P, Papadopoulos GL, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K (2009) DIANA-microT web server: elucidating microRNA functions through target prediction. Nucleic Acids Res 37(Web Server issue):W273–W276PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    Maragkakis M, Vergoulis T, Alexiou P, Reczko M, Plomaritou K, Gousis M, Kourtis K, Koziris N, Dalamagas T, Hatzigeorgiou AG (2011) DIANA-microT web server upgrade supports Fly and worm miRNA target prediction and bibliographic miRNA to disease association. Nucleic Acids Res 39(suppl 2):W145–W148PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2011) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40(D1):D109–D114PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Liu C, Mallick B, Long D, Rennie WA, Wolenc A, Carmack CS, Ding Y (2013) CLIP-based prediction of mammalian microRNA binding sites. Nucleic Acids Res 41(14):e138–e138PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Hsu PWC, Huang H-D, Hsu S-D, Lin L-Z, Tsou A-P, Tseng C-P, Stadler PF, Washietl S, Hofacker IL (2006) miRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomes. Nucleic Acids Res 34(suppl 1):D135–D139PubMedCrossRefGoogle Scholar
  55. 55.
    Hsu S-D, Chu C-H, Tsou A-P, Chen S-J, Chen H-C, Hsu PW-C, Wong Y-H, Chen Y-H, Chen G-H, Huang H-D (2008) miRNAMap 2.0: genomic maps of microRNAs in metazoan genomes. Nucleic Acids Res 36(suppl 1):D165–D169PubMedGoogle Scholar
  56. 56.
    Friedman Y, Naamati G, Linial M (2010) MiRror: a combinatorial analysis web tool for ensembles of microRNAs and their targets. Bioinformatics 26(15):1920–1921PubMedCrossRefGoogle Scholar
  57. 57.
    Friedman Y, Karsenty S, Linial M (2014) miRror-Suite: decoding coordinated regulation by microRNAs. Database 2014:bau043PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Hsu JBK, Chiu C-M, Hsu S-D, Huang W-Y, Chien C-H, Lee T-Y, Huang H-D (2011) miRTar: an integrated system for identifying miRNA-target interactions in human. BMC Bioinformatics 12(1):300PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Dweep H, Sticht C, Pandey P, Gretz N (2011) miRWalk - database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform 44(5):839–847PubMedCrossRefGoogle Scholar
  60. 60.
    Shirdel EA, Xie W, Mak TW, Jurisica I (2011) NAViGaTing the micronome - using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. PLoS One 6(2):e17429PubMedPubMedCentralCrossRefGoogle Scholar
  61. 61.
    Coronnello C, Benos PV (2013) ComiR: combinatorial microRNA target prediction tool. Nucleic Acids Res 41(W1):W159–W164PubMedPubMedCentralCrossRefGoogle Scholar
  62. 62.
    Wang P, Ning S, Wang Q, Li R, Ye J, Zhao Z, Li Y, Huang T, Li X (2013) mirTarPri: improved prioritization of MicroRNA targets through incorporation of functional genomics data. PLoS One 8(1):e53685PubMedPubMedCentralCrossRefGoogle Scholar
  63. 63.
    Vejnar CE, Zdobnov EM (2012) miRmap: comprehensive prediction of microRNA target repression strength. Nucleic Acids Res 40(22):11673–11683PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Vejnar CE, Blum M, Zdobnov EM (2013) miRmap web: comprehensive microRNA target prediction online. Nucleic Acids Res 41(W1):W165–W168PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Wu C, Bardes EE, Jegga AG, Aronow BJ (2014) ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context. Nucleic Acids Res 42(Web Server issue):W107–W113. gku409PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Ritchie W, Flamant S, Rasko JE (2009) Predicting microRNA targets and functions: traps for the unwary. Nat Methods 6(6):397PubMedCrossRefGoogle Scholar
  67. 67.
    Farh KK-H, Grimson A, Jan C, Lewis BP, Johnston WK, Lim LP, Burge CB, Bartel DP (2005) The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science 310(5755):1817–1821PubMedCrossRefGoogle Scholar
  68. 68.
    Reczko M, Maragkakis M, Alexiou P, Grosse I, Hatzigeorgiou AG (2012) Functional microRNA targets in protein coding sequences. Bioinformatics 28(6):771–776PubMedCrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Most Mauluda Akhtar
    • 5
  • Luigina Micolucci
    • 1
    • 2
  • Md Soriful Islam
    • 3
  • Fabiola Olivieri
    • 1
    • 4
  • Antonio Domenico Procopio
    • 1
    • 4
  1. 1.Laboratory of Experimental Pathology, Department of Clinical and Molecular SciencesUniversità Politecnica delle MarcheAnconaItaly
  2. 2.Computational Pathology Unit, Department of Clinical and Molecular SciencesUniversità Politecnica delle MarcheAnconaItaly
  3. 3.Department of Gynecology and ObstetricsJohns Hopkins University, School of MedicineBaltimoreUSA
  4. 4.Center of Clinical Pathology and Innovative TherapiesItalian National Research Center on Aging (INRCA-IRCCS)AnconaItaly
  5. 5.BioinformaticsAsian University for WomenChattogramBangladesh

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