Abstract
Computational prediction of microRNA (miRNA) targets is a fundamental step towards the characterization of miRNA function and the understanding of their role in disease. A single miRNA can regulate hundreds of different gene transcripts through partial sequence complementarity and a single gene may be regulated by several miRNAs acting cooperatively. The remarkable advances made in recent years have allowed the identification of key features for functional miRNA binding sites. A plethora of prediction tools are now available, but their accuracies remain rather poor, as miRNA target recognition has revealed itself to be a very complex and dynamic mechanism, still only partially understood.
In this chapter, the principles of miRNA target prediction in animals are presented, together with the most up-to-date and effective computational approaches and tools available.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Bulges are unpaired stretches of nucleotides located within one strand of a nucleic acid duplex.
- 2.
miRNAs encoded within the introns of coding genes.
- 3.
ceRNA are coding or noncoding transcripts that regulate other transcripts by competing for shared miRNAs.
- 4.
The gene level test consisted in the prediction of interaction between mRNAs and a given miRNA. The duplex level test consisted in the prediction of interaction between a given fragment of mRNA and a given miRNA.
References
Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–97.
Bartel DP. microRNAs: target recognition and regulatory functions. Cell. 2009;136:215–33. doi:10.1016/j.cell.2009.01.002.
Kozomara A, Griffiths-Jones S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011;39:D152–7.
Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2013;42:D68–73. doi:10.1093/nar/gkt1181.
Du T. microPrimer: the biogenesis and function of microRNA. Development. 2005;132:4645–52. doi:10.1242/dev.02070.
Pillai RS, Bhattacharyya SN, Artus CG, et al. Inhibition of translational initiation by Let-7 MicroRNA in human cells. Science. 2005;309:1573–6. doi:10.1126/science.1115079.
Maroney PA, Yu Y, Fisher J, Nilsen TW. Evidence that microRNAs are associated with translating messenger RNAs in human cells. Nat Struct Mol Biol. 2006;13:1102–7. doi:10.1038/nsmb1174.
Eulalio A, Huntzinger E, Nishihara T, et al. Deadenylation is a widespread effect of miRNA regulation. RNA. 2009;15:21–32. doi:10.1261/rna.1399509.
Wu L, Fan J, Belasco JG. MicroRNAs direct rapid deadenylation of mRNA. Proc Natl Acad Sci. 2006;103:4034–9. doi:10.1073/pnas.0510928103.
Meister G, Landthaler M, Patkaniowska A, et al. Human Argonaute2 mediates RNA cleavage targeted by miRNAs and siRNAs. Mol Cell. 2004;15:185–97. doi:10.1016/j.molcel.2004.07.007.
Tuschl T, Zamore PD, Lehmann R, et al. Targeted mRNA degradation by double-stranded RNA in vitro. Genes Dev. 1999;13:3191–7.
Selbach M, Schwanhäusser B, Thierfelder N, et al. Widespread changes in protein synthesis induced by microRNAs. Nature. 2008;455:58–63. doi:10.1038/nature07228.
Baek D, Villén J, Shin C, et al. The impact of microRNAs on protein output. Nature. 2008;455:64–71. doi:10.1038/nature07242.
Guo H, Ingolia NT, Weissman JS, Bartel DP. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature. 2010;466:835–40. doi:10.1038/nature09267.
Tarang S, Weston MD. Macros in microRNA target identification: a comparative analysis of in silico, in vitro, and in vivo approaches to microRNA target identification. RNA Biol. 2014;11(4):324–33. doi:10.4161/rna.28649.
Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005;120:15–20. doi:10.1016/j.cell.2004.12.035.
Friedman RC, Farh KK-H, Burge CB, Bartel DP. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2008;19:92–105. doi:10.1101/gr.082701.108.
Ding J, Zhou S, Guan J. miRFam: an effective automatic miRNA classification method based on n-grams and a multiclass SVM. BMC Bioinformatics. 2011;12:216. doi:10.1186/1471-2105-12-216.
Zou Q, Mao Y, Hu L, et al. miRClassify: an advanced web server for miRNA family classification and annotation. Comput Biol Med. 2014;45:157–60. doi:10.1016/j.compbiomed.2013.12.007.
Grimson A. A targeted approach to miRNA target identification. Nat Methods. 2010;7:795–7. doi:10.1038/nmeth1010-795.
Garcia DM, Baek D, Shin C, et al. Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat Struct Mol Biol. 2011;18:1139–46. doi:10.1038/nsmb.2115.
Brennecke J, Stark A, Russell RB, Cohen SM. Principles of microRNA-target recognition. PLoS Biol. 2005;3, e85. doi:10.1371/journal.pbio.0030085.
Krek A, Grün D, Poy MN, et al. Combinatorial microRNA target predictions. Nat Genet. 2005;37:495–500. doi:10.1038/ng1536.
Grimson A, Farh KK-H, Johnston WK, et al. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell. 2007;27:91–105. doi:10.1016/j.molcel.2007.06.017.
Peterson SM, Thompson JA, Ufkin ML, et al. Common features of microRNA target prediction tools. Front Genet. 2014;5:23. doi:10.3389/fgene.2014.00023.
Cloonan N. Re-thinking miRNA-mRNA interactions: intertwining issues confound target discovery. Bioessays. 2015;37:379–88. doi:10.1002/bies.201400191.
Lagana A, Acunzo M, Romano G, et al. miR-Synth: a computational resource for the design of multi-site multi-target synthetic miRNAs. Nucleic Acids Res. 2014;42:5416–25. doi:10.1093/nar/gku202.
Shin C, Nam J-W, Farh KK-H, et al. Expanding the microRNA targeting code: functional sites with centered pairing. Mol Cell. 2010;38:789–802. doi:10.1016/j.molcel.2010.06.005.
Chi SW, Hannon GJ, Darnell RB. An alternative mode of microRNA target recognition. Nat Struct Mol Biol. 2012;19:321–7. doi:10.1038/nsmb.2230.
Hafner M, Lianoglou S, Tuschl T, Betel D. Genome-wide identification of miRNA targets by PAR-CLIP. Methods. 2012;58:94–105. doi:10.1016/j.ymeth.2012.08.006.
Loeb GB, Khan AA, Canner D, et al. Transcriptome-wide miR-155 binding map reveals widespread noncanonical microRNA targeting. Mol Cell. 2012;48:760–70. doi:10.1016/j.molcel.2012.10.002.
Martin HC, Wani S, Steptoe AL, et al. Imperfect centered miRNA binding sites are common and can mediate repression of target mRNAs. Genome Biol. 2014;15:R51. doi:10.1186/gb-2014-15-3-r51.
Helwak A, Kudla G, Dudnakova T, Tollervey D. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell. 2013;153:654–65. doi:10.1016/j.cell.2013.03.043.
John B, Enright AJ, Aravin A, et al. Human microRNA targets. PLoS Biol. 2004;2, e363. doi:10.1371/journal.pbio.0020363.
Enright AJ, John B, Gaul U, et al. MicroRNA targets in Drosophila. Genome Biol. 2003;5:R1–14. doi:10.1186/gb-2003-5-1-r1.
Betel D, Koppal A, Agius P, et al. Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol. 2010;11:R90. doi:10.1186/gb-2010-11-8-r90.
Khorshid M, Hausser J, Zavolan M, van Nimwegen E. A biophysical miRNA-mRNA interaction model infers canonical and noncanonical targets. Nat Methods. 2013;10:253–5. doi:10.1038/nmeth.2341.
Menor M, Ching T, Zhu X, et al. mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome Biol. 2014;15(10):500. doi:10.1186/s13059-014-0500-5.
Farh KK-H, Grimson A, Jan C, et al. The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science. 2005;310:1817–21. doi:10.1126/science.1121158.
Lall S, Grün D, Krek A, et al. A genome-wide map of conserved microRNA targets in C. elegans. Curr Biol. 2006;16:460–71. doi:10.1016/j.cub.2006.01.050.
Bandyopadhyay S, Ghosh D, Mitra R, Zhao Z. MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets. Sci Rep. 2015;5:8004–12. doi:10.1038/srep08004.
Reczko M, Maragkakis M, Alexiou P, et al. Functional microRNA targets in protein coding sequences. Bioinformatics. 2012;28:771–6. doi:10.1093/bioinformatics/bts043.
Marin RM, Sulc M, Vanicek J. Searching the coding region for microRNA targets. RNA. 2013;19:467–74. doi:10.1261/rna.035634.112.
Schnall-Levin M, Zhao Y, Perrimon N, Berger B. Conserved microRNA targeting in Drosophila is as widespread in coding regions as in 3′UTRs. Proc Natl Acad Sci U S A. 2010;107:15751–6. doi:10.1073/pnas.1006172107.
Lorenz R, Bernhart SH, Höner Zu Siederdissen C, et al. ViennaRNA Package 2.0. Algorithms Mol Biol. 2011;6:26. doi:10.1186/1748-7188-6-26.
Rehmsmeier M. Fast and effective prediction of microRNA/target duplexes. RNA. 2004;10:1507–17. doi:10.1261/rna.5248604.
Lagana A, Forte S, Russo F, et al. Prediction of human targets for viral-encoded microRNAs by thermodynamics and empirical constraints. J RNAi Gene Silenc. 2010;6:379–85.
Bernhart SH, Hofacker IL, Stadler PF. Local RNA base pairing probabilities in large sequences. Bioinformatics. 2006;22:614–5. doi:10.1093/bioinformatics/btk014.
Long D, Lee R, Williams P, et al. Potent effect of target structure on microRNA function. Nat Struct Mol Biol. 2007;14:287–94. doi:10.1038/nsmb1226.
Kertesz M, Iovino N, Unnerstall U, et al. The role of site accessibility in microRNA target recognition. Nat Genet. 2007;39:1278–84. doi:10.1038/ng2135.
Vejnar CE, Zdobnov EM. miRmap: comprehensive prediction of microRNA target repression strength. Nucleic Acids Res. 2012;40:11673–83. doi:10.1093/nar/gks901.
Gan HH, Gunsalus KC. Tertiary structure-based analysis of microRNA-target interactions. RNA. 2013;19:539–51. doi:10.1261/rna.035691.112.
Bazzini AA, Lee MT, Giraldez AJ. Ribosome profiling shows that miR-430 reduces translation before causing mRNA decay in zebrafish. Science. 2012;336:233–7. doi:10.1126/science.1215704.
Djuranovic S, Nahvi A, Green R. miRNA-mediated gene silencing by translational repression followed by mRNA deadenylation and decay. Science. 2012;336:237–40. doi:10.1126/science.1215691.
Huang JC, Babak T, Corson TW, et al. Using expression profiling data to identify human microRNA targets. Nat Methods. 2007;4:1045–9. doi:10.1038/nmeth1130.
Gennarino VA, Sardiello M, Mutarelli M, et al. HOCTAR database: a unique resource for microRNA target prediction. Gene. 2011;480:51–8. doi:10.1016/j.gene.2011.03.005.
Muniategui A, Nogales-Cadenas R, Vázquez M, et al. Quantification of miRNA-mRNA interactions. PLoS One. 2012;7:e30766. doi:10.1371/journal.pone.0030766.
Bossel Ben-Moshe N, Avraham R, Kedmi M, et al. Context-specific microRNA analysis: identification of functional microRNAs and their mRNA targets. Nucleic Acids Res. 2012;40:10614–27. doi:10.1093/nar/gks841.
Hammell M, Long D, Zhang L, et al. mirWIP: microRNA target prediction based on microRNA-containing ribonucleoprotein–enriched transcripts. Nat Methods. 2008;5:813–9. doi:10.1038/nmeth.1247.
Liu C, Mallick B, Long D, et al. CLIP-based prediction of mammalian microRNA binding sites. Nucleic Acids Res. 2013;41:e138. doi:10.1093/nar/gkt435.
Chou C-H, Lin F-M, Chou M-T, et al. A computational approach for identifying microRNA-target interactions using high-throughput CLIP and PAR-CLIP sequencing. BMC Genomics. 2013;14:S2. doi:10.1186/1471-2164-14-S1-S2.
Hsu S-D, Tseng Y-T, Shrestha S, et al. miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 2014;42:D78–85. doi:10.1093/nar/gkt1266.
Anders G, Mackowiak SD, Jens M, et al. doRiNA: a database of RNA interactions in post-transcriptional regulation. Nucleic Acids Res. 2011;40:D180–6. doi:10.1093/nar/gkr1007.
Yang J-H, Li J-H, Shao P, et al. starBase: a database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res. 2010;39:D202–9. doi:10.1093/nar/gkq1056.
Li J-H, Liu S, Zhou H, et al. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2013;42:D92–7. doi:10.1093/nar/gkt1248.
Salmena L, Poliseno L, Tay Y, et al. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell. 2011;146:353–8. doi:10.1016/j.cell.2011.07.014.
Poliseno L, Salmena L, Zhang J, et al. A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature. 2010;465:1033–8. doi:10.1038/nature09144.
Sen R, Ghosal S, Das S, et al. Competing endogenous RNA: the key to posttranscriptional regulation. Sci World J. 2014;2014:1–6. doi:10.1155/2014/896206.
Vlachos IS, Paraskevopoulou MD, Karagkouni D, et al. DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res. 2015;43:D153–9. doi:10.1093/nar/gku1215.
Xiao F, Zuo Z, Cai G, et al. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2009;37:D105–10. doi:10.1093/nar/gkn851.
Shirdel EA, Xie W, Mak TW, Jurisica I. NAViGaTing the micronome—using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. PLoS One. 2011;6:e17429. doi:10.1371/journal.pone.0017429.
Lagana A, Forte S, Giudice A, et al. MiRo: a miRNA knowledge base. Database. 2009;2009:bap008. doi:10.1093/database/bap008.
Cho S, Jang I, Jun Y, et al. miRGator v3.0: a microRNA portal for deep sequencing, expression profiling and mRNA targeting. Nucleic Acids Res. 2012;41:D252–7. doi:10.1093/nar/gks1168.
Dweep H, Sticht C, Pandey P, Gretz N. MiRWalk—database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform. 2011;44:839–47. doi:10.1016/j.jbi.2011.05.002.
Guo Z, Maki M, Ding R, et al. Genome-wide survey of tissue-specific microRNA and transcription factor regulatory networks in 12 tissues. Sci Rep. 2014;4:1–9. doi:10.1038/srep05150.
Fan X, Kurgan L. Comprehensive overview and assessment of computational prediction of microRNA targets in animals. Brief Bioinformatics. 2014:bbu044. doi: 10.1093/bib/bbu044
Coronnello C, Benos PV. ComiR: combinatorial microRNA target prediction tool. Nucleic Acids Res. 2013;41:W159–64. doi:10.1093/nar/gkt379.
Coronnello C, Hartmaier R, Arora A, et al. Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density. PLoS Comput Biol. 2012;8:e1002830. doi:10.1371/journal.pcbi.1002830.
Paraskevopoulou MD, Georgakilas G, Kostoulas N, et al. DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows. Nucleic Acids Res. 2013;41:W169–73. doi:10.1093/nar/gkt393.
Gaidatzis D, van Nimwegen E, Hausser J, Zavolan M. Inference of miRNA targets using evolutionary conservation and pathway analysis. BMC Bioinformatics. 2007;8:248. doi:10.1186/1471-2105-8-69.
Stark A, Brennecke J, Russell RB, Cohen SM. Identification of Drosophila microRNA targets. PLoS Biol. 2003;1:e60. doi:10.1371/journal.pbio.0000060.
Gamazon ER, Im H-K, Duan S, et al. ExprTarget: an integrative approach to predicting human microRNA targets. PLoS One. 2010;5:e13534–8. doi:10.1371/journal.pone.0013534.
Ye W, Lv Q, Wong C-KA, et al. The effect of central loops in miRNA: MRE duplexes on the efficiency of miRNA-mediated gene regulation. PLoS One. 2008;3:e1719. doi:10.1371/journal.pone.0001719.
Rusinov V, Baev V, Minkov IN, Tabler M. MicroInspector: a web tool for detection of miRNA binding sites in an RNA sequence. Nucleic Acids Res. 2005;33:W696–700. doi:10.1093/nar/gki364.
Thadani R, Tammi MT. MicroTar: predicting microRNA targets from RNA duplexes. BMC Bioinformatics. 2006;7:S20–9. doi:10.1186/1471-2105-7-S5-S20.
Jeggari A, Marks DS, Larsson E. miRcode: a map of putative microRNA target sites in the long non-coding transcriptome. Bioinformatics. 2012;28:2062–3. doi:10.1093/bioinformatics/bts344.
Wang X, El Naqa IM. Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics. 2008;24:325–32. doi:10.1093/bioinformatics/btm595.
Mitra R, Bandyopadhyay S. MultiMiTar: a novel multi objective optimization based miRNA-target prediction method. PLoS One. 2011;6, e24583. doi:10.1371/journal.pone.0024583.s012.
Marin RM, Vanicek J. Efficient use of accessibility in microRNA target prediction. Nucleic Acids Res. 2011;39:19–29. doi:10.1093/nar/gkq768.
Marín RM, Vaníček J. Optimal use of conservation and accessibility filters in microRNA target prediction. PLoS One. 2012;7, e32208. doi:10.1371/journal.pone.0032208.
Oğul H, Umu SU, Tuncel YY, Akkaya MS. A probabilistic approach to microRNA-target binding. Biochem Biophys Res Commun. 2011;413:111–5. doi:10.1016/j.bbrc.2011.08.065.
Elefant N, Berger A, Shein H, et al. RepTar: a database of predicted cellular targets of host and viral miRNAs. Nucleic Acids Res. 2011;39:D188–94. doi:10.1093/nar/gkq1233.
Miranda KC, Huynh T, Tay Y, et al. A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes. Cell. 2006;126:1203–17. doi:10.1016/j.cell.2006.07.031.
Liu H, Yue D, Chen Y, et al. Improving performance of mammalian microRNA target prediction. BMC Bioinformatics. 2010;11:476. doi:10.1186/1471-2105-11-476.
Bandyopadhyay S, Mitra R. TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics. 2009;25:2625–31. doi:10.1093/bioinformatics/btp503.
Sturm M, Hackenberg M, Langenberger D, Frishman D. TargetSpy: a supervised machine learning approach for microRNA target prediction. BMC Bioinformatics. 2010;11:292. doi:10.1186/1471-2105-11-292.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Laganà, A. (2015). Computational Prediction of microRNA Targets. In: Santulli, G. (eds) microRNA: Basic Science. Advances in Experimental Medicine and Biology, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-319-22380-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-22380-3_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22379-7
Online ISBN: 978-3-319-22380-3
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)