The binding of microRNAs (miRNAs) to their target sites is a complex process, mediated by the Argonaute (Ago) family of proteins. The prediction of miRNA:target site binding is an important first step for any miRNA target prediction algorithm. To date, the potential for miRNA:target site binding is evaluated using either co-folding free energy measures or heuristic approaches, based on the identification of binding 'seeds', i.e., continuous stretches of binding corresponding to specific parts of the miRNA. The limitations of both these families of methods have produced generations of miRNA target prediction algorithms that are primarily focused on 'canonical' seed targets, even though unbiased experimental methods have shown that only approximately half of in vivo miRNA targets are 'canonical'. Herein, we present miRBind, a deep learning method and web server that can be used to accurately predict the potential of miRNA:target site binding. We trained our method using seed-agnostic experimental data and show that our method outperforms both seed-based approaches and co-fold free energy approaches. The full code for the development of miRBind and a freely accessible web server are freely available.

Central European Institute of Technology Masaryk University 60177 Brno Czech Republic

Faculty of Informatics Masaryk University 60200 Brno Czech Republic

Faculty of Science National Centre for Biomolecular Research Masaryk University 61137 Brno Czech Republic

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Bartel D.P. Metazoan MicroRNAs. Cell. 2018;173:20–51. doi: 10.1016/j.cell.2018.03.006. PubMed DOI PMC

Lee R.C., Feinbaum R.L., Ambros V. The C. Elegans Heterochronic Gene Lin-4 Encodes Small RNAs with Antisense Complementarity to Lin-14. Cell. 1993;75:843–854. doi: 10.1016/0092-8674(93)90529-Y. PubMed DOI

Wightman B., Ha I., Ruvkun G. Posttranscriptional Regulation of the Heterochronic Gene Lin-14 by Lin-4 Mediates Temporal Pattern Formation in C. Elegans. Cell. 1993;75:855–862. doi: 10.1016/0092-8674(93)90530-4. PubMed DOI

Pasquinelli A.E., Reinhart B.J., Slack F., Martindale M.Q., Kuroda M.I., Maller B., Hayward D.C., Ball E.E., Degnan B., Müller P., et al. Conservation of the Sequence and Temporal Expression of Let-7 Heterochronic Regulatory RNA. Nature. 2000;408:86–89. doi: 10.1038/35040556. PubMed DOI

Kozomara A., Griffiths-Jones S. MiRBase: Integrating MicroRNA Annotation and Deep-Sequencing Data. Nucleic Acids Res. 2011;39:D152–D157. doi: 10.1093/nar/gkq1027. PubMed DOI PMC

Adams L. Pri-MiRNA Processing: Structure Is Key. Nat. Rev. Genet. 2017;18:145. doi: 10.1038/nrg.2017.6. PubMed DOI

Lund E., Güttinger S., Calado A., Dahlberg J.E., Kutay U. Nuclear Export of MicroRNA Precursors. Science. 2004;303:95–98. doi: 10.1126/science.1090599. PubMed DOI

O’Brien J., Hayder H., Zayed Y., Peng C. Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation. Front. Endocrinol. 2018;9:402. doi: 10.3389/fendo.2018.00402. PubMed DOI PMC

Saliminejad K., Khorram Khorshid H.R., Soleymani Fard S., Ghaffari S.H. An Overview of MicroRNAs: Biology, Functions, Therapeutics, and Analysis Methods. J. Cell. Physiol. 2019;234:5451–5465. doi: 10.1002/jcp.27486. PubMed DOI

Filipowicz W., Bhattacharyya S.N., Sonenberg N. Mechanisms of Post-Transcriptional Regulation by MicroRNAs: Are the Answers in Sight? Nat. Rev. Genet. 2008;9:102–114. doi: 10.1038/nrg2290. PubMed DOI

Dueck A., Ziegler C., Eichner A., Berezikov E., Meister G. MicroRNAs Associated with the Different Human Argonaute Proteins. Nucleic Acids Res. 2012;40:9850–9862. doi: 10.1093/nar/gks705. PubMed DOI PMC

Pasquinelli A.E. MicroRNAs and Their Targets: Recognition, Regulation and an Emerging Reciprocal Relationship. Nat. Rev. Genet. 2012;13:271–282. doi: 10.1038/nrg3162. PubMed DOI

Kalla R., Ventham N.T., Kennedy N.A., Quintana J.F., Nimmo E.R., Buck A.H., Satsangi J. MicroRNAs: New Players in IBD. Gut. 2015;64:504–513. doi: 10.1136/gutjnl-2014-307891. PubMed DOI PMC

Zealy R.W., Wrenn S.P., Davila S., Min K.-W., Yoon J.-H. MicroRNA-Binding Proteins: Specificity and Function. WIREs RNA. 2017;8:e1414. doi: 10.1002/wrna.1414. PubMed DOI

Lewis B.P., Shih I.-H., Jones-Rhoades M.W., Bartel D.P., Burge C.B. Prediction of Mammalian MicroRNA Targets. Cell. 2003;115:787–798. doi: 10.1016/S0092-8674(03)01018-3. PubMed DOI

Bartel D.P. MicroRNA Target Recognition and Regulatory Functions. Cell. 2009;136:215–233. doi: 10.1016/j.cell.2009.01.002. PubMed DOI PMC

Broughton J.P., Lovci M.T., Huang J.L., Yeo G.W., Pasquinelli A.E. Pairing Beyond the Seed Supports MicroRNA Targeting Specificity. Mol. Cell. 2016;64:320–333. doi: 10.1016/j.molcel.2016.09.004. PubMed DOI PMC

Agarwal V., Bell G.W., Nam J.-W., Bartel D.P. Predicting Effective MicroRNA Target Sites in Mammalian MRNAs. eLife. 2015;4:e05005. doi: 10.7554/eLife.05005. PubMed DOI PMC

Kudla G., Granneman S., Hahn D., Beggs J.D., Tollervey D. Cross-Linking, Ligation, and Sequencing of Hybrids Reveals RNA–RNA Interactions in Yeast. Proc. Natl. Acad. Sci. USA. 2011;108:10010–10015. doi: 10.1073/pnas.1017386108. PubMed DOI PMC

Helwak A., Kudla G., Dudnakova T., Tollervey D. Mapping the Human MiRNA Interactome by CLASH Reveals Frequent Noncanonical Binding. Cell. 2013;153:654–665. doi: 10.1016/j.cell.2013.03.043. PubMed DOI PMC

John B., Enright A.J., Aravin A., Tuschl T., Sander C., Marks D.S. Human MicroRNA Targets. PLoS Biol. 2004;2:e363. doi: 10.1371/journal.pbio.0020363. PubMed DOI PMC

Enright A.J., John B., Gaul U., Tuschl T., Sander C., Marks D.S. MicroRNA Targets in Drosophila. Genome Biol. 2004;5:R1. doi: 10.1186/gb-2003-5-1-r1. PubMed DOI PMC

Kertesz M., Iovino N., Unnerstall U., Gaul U., Segal E. The Role of Site Accessibility in MicroRNA Target Recognition. Nat. Genet. 2007;39:1278–1284. doi: 10.1038/ng2135. PubMed DOI

Baek D., Villén J., Shin C., Camargo F.D., Gygi S.P., Bartel D.P. The Impact of MicroRNAs on Protein Output. Nature. 2008;455:64–71. doi: 10.1038/nature07242. PubMed DOI PMC

Selbach M., Schwanhäusser B., Thierfelder N., Fang Z., Khanin R., Rajewsky N. Widespread Changes in Protein Synthesis Induced by MicroRNAs. Nature. 2008;455:58–63. doi: 10.1038/nature07228. PubMed DOI

Alexiou P., Maragkakis M., Papadopoulos G.L., Reczko M., Hatzigeorgiou A.G. Lost in Translation: An Assessment and Perspective for Computational MicroRNA Target Identification. Bioinformatics. 2009;25:3049–3055. doi: 10.1093/bioinformatics/btp565. PubMed DOI

Ule J., Jensen K.B., Ruggiu M., Mele A., Ule A., Darnell R.B. CLIP Identifies Nova-Regulated RNA Networks in the Brain. Science. 2003;302:1212–1215. doi: 10.1126/science.1090095. PubMed DOI

Karagkouni D., Paraskevopoulou M.D., Chatzopoulos S., Vlachos I.S., Tastsoglou S., Kanellos I., Papadimitriou D., Kavakiotis I., Maniou S., Skoufos G., et al. DIANA-TarBase v8: A Decade-Long Collection of Experimentally Supported MiRNA–Gene Interactions. Nucleic Acids Res. 2018;46:D239–D245. doi: 10.1093/nar/gkx1141. PubMed DOI PMC

Helwak A., Tollervey D. Mapping the MiRNA Interactome by Cross-Linking Ligation and Sequencing of Hybrids (CLASH) Nat. Protoc. 2014;9:711–728. doi: 10.1038/nprot.2014.043. PubMed DOI PMC

Moore M.J., Scheel T.K.H., Luna J.M., Park C.Y., Fak J.J., Nishiuchi E., Rice C.M., Darnell R.B. MiRNA–Target Chimeras Reveal MiRNA 3′-End Pairing as a Major Determinant of Argonaute Target Specificity. Nat. Commun. 2015;6:8864. doi: 10.1038/ncomms9864. PubMed DOI PMC

Riolo G., Cantara S., Marzocchi C., Ricci C. MiRNA Targets: From Prediction Tools to Experimental Validation. Methods Protoc. 2020;4:1. doi: 10.3390/mps4010001. PubMed DOI PMC

Peterson S.M., Thompson J.A., Ufkin M.L., Sathyanarayana P., Liaw L., Congdon C.B. Common Features of MicroRNA Target Prediction Tools. Front. Genet. 2014;5:23. doi: 10.3389/fgene.2014.00023. PubMed DOI PMC

Ekimler S., Sahin K. Computational Methods for MicroRNA Target Prediction. Genes. 2014;5:671–683. doi: 10.3390/genes5030671. PubMed DOI PMC

Shaker F., Nikravesh A., Arezumand R., Aghaee-Bakhtiari S.H. Web-based tools for miRNA studies analysis. Comput. Biol. Med. 2020;127:104060. doi: 10.1016/j.compbiomed.2020.104060. PubMed DOI

Betel D., Koppal A., Agius P., Sander C., Leslie C. 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. PubMed DOI PMC

Maragkakis M., Reczko M., Simossis V.A., Alexiou P., Papadopoulos G.L., Dalamagas T., Giannopoulos G., Goumas G., Koukis E., Kourtis K., et al. DIANA-microT web server: Elucidating microRNA functions through target prediction. Nucleic Acids Res. 2009;37:W273–W276. doi: 10.1093/nar/gkp292. PubMed DOI PMC

Reczko M., Maragkakis M., Alexiou P., Grosse I., Hatzigeorgiou A.G. Functional microRNA targets in protein coding sequences. Bioinformatics. 2012;28:771–776. doi: 10.1093/bioinformatics/bts043. PubMed DOI

Paraskevopoulou M.D., Georgakilas G., Kostoulas N., Vlachos I.S., Vergoulis T., Reczko M., Filippidis C., Dalamagas T., Hatzigeorgiou A.G. DIANA-microT web server v5.0: Service integration into miRNA functional analysis workflows. Nucleic Acids Res. 2013;41:W169–W173. doi: 10.1093/nar/gkt393. PubMed DOI PMC

Wang X., El Naqa I.M. Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics. 2008;24:325–332. doi: 10.1093/bioinformatics/btm595. PubMed DOI

Bandyopadhyay S., Mitra R. TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics. 2009;25:2625–2631. doi: 10.1093/bioinformatics/btp503. PubMed DOI

Liu H., Yue D., Chen Y., Gao S.-J., Huang Y. Improving performance of mammalian microRNA target prediction. BMC Bioinform. 2010;11:476. doi: 10.1186/1471-2105-11-476. PubMed DOI PMC

Eraslan G., Avsec Ž., Gagneur J., Theis F.J. Deep Learning: New Computational Modelling Techniques for Genomics. Nat. Rev. Genet. 2019;20:389–403. doi: 10.1038/s41576-019-0122-6. PubMed DOI

LeCun Y., Bengio Y., Hinton G. Deep Learning. Nature. 2015;521:436–444. doi: 10.1038/nature14539. PubMed DOI

Min S., Lee B., Yoon S. Deep Learning in Bioinformatics. Brief. Bioinform. 2017;18:851–869. doi: 10.1093/bib/bbw068. PubMed DOI

He K., Zhang X., Ren S., Sun J. Deep Residual Learning for Image Recognition; Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Las Vegas, NV, USA. 27–30 June 2016; pp. 770–778. DOI

Travis A.J., Moody J., Helwak A., Tollervey D., Kudla G. Hyb: A Bioinformatics Pipeline for the Analysis of CLASH (Crosslinking, Ligation and Sequencing of Hybrids) Data. Methods. 2014;65:263–273. doi: 10.1016/j.ymeth.2013.10.015. PubMed DOI PMC

Manakov S.A., Shishkin A.A., Yee B.A., Shen K.A., Cox D.C., Park S.S., Foster H.M., Chapman K.B., Yeo G.W., Nostrand E.L.V. Scalable and Deep Profiling of MRNA Targets for Individual MicroRNAs with Chimeric ECLIP. bioRxiv. 2022 doi: 10.1101/2022.02.13.480296. DOI

Database Resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2017;45:D12–D17. doi: 10.1093/nar/gkw1071. PubMed DOI PMC

Cunningham F., Allen J.E., Allen J., Alvarez-Jarreta J., Amode M.R., Armean I.M., Austine-Orimoloye O., Azov A.G., Barnes I., Bennett R., et al. Ensembl 2022. Nucleic Acids Res. 2022;50:D988–D995. doi: 10.1093/nar/gkab1049. PubMed DOI PMC

Haeussler M., Zweig A.S., Tyner C., Speir M.L., Rosenbloom K.R., Raney B.J., Lee C.M., Lee B.T., Hinrichs A.S., Gonzalez J.N., et al. The UCSC Genome Browser Database: 2019 Update. Nucleic Acids Res. 2019;47:D853–D858. doi: 10.1093/nar/gky1095. PubMed DOI PMC

Ji Y., Zhou Z., Liu H., Davuluri R.V. DNABERT: Pre-Trained Bidirectional Encoder Representations from Transformers Model for DNA-Language in Genome. Bioinformatics. 2021;37:2112–2120. doi: 10.1093/bioinformatics/btab083. PubMed DOI PMC

Georgakilas G.K., Grioni A., Liakos K.G., Chalupova E., Plessas F.C., Alexiou P. Multi-Branch Convolutional Neural Network for Identification of Small Non-Coding RNA Genomic Loci. Sci. Rep. 2020;10:9486. doi: 10.1038/s41598-020-66454-3. PubMed DOI PMC

Guo H., Viktor H.L. Learning from Imbalanced Data Sets with Boosting and Data Generation: The DataBoost-IM Approach. SIGKDD Explor. Newsl. 2004;6:30–39. doi: 10.1145/1007730.1007736. DOI

Smith M.R., Martinez T., Giraud-Carrier C. An Instance Level Analysis of Data Complexity. Mach Learn. 2014;95:225–256. doi: 10.1007/s10994-013-5422-z. DOI

Krüger J., Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res. 2006;34:W451–W454. doi: 10.1093/nar/gkl243. PubMed DOI PMC

Bernhart S.H., Tafer H., Mückstein U., Flamm C., Stadler P.F., Hofacker I.L. Partition Function and Base Pairing Probabilities of RNA Heterodimers. Algorithms Mol. Biol. 2006;1:3. doi: 10.1186/1748-7188-1-3. PubMed DOI PMC

Lorenz R., Bernhart S.H., Höner zu Siederdissen C., Tafer H., Flamm C., Stadler P.F., Hofacker I.L. ViennaRNA Package 2.0. Algorithms Mol. Biol. 2011;6:26. doi: 10.1186/1748-7188-6-26. PubMed DOI PMC

Saito T., Rehmsmeier M. The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLoS ONE. 2015;10:e0118432. doi: 10.1371/journal.pone.0118432. PubMed DOI PMC

Miranda K.C., Huynh T., Tay Y., Ang Y.-S., Tam W.-L., Thomson A.M., Lim B., Rigoutsos I. A Pattern-Based Method for the Identification of MicroRNA Binding Sites and Their Corresponding Heteroduplexes. Cell. 2006;126:1203–1217. doi: 10.1016/j.cell.2006.07.031. PubMed DOI

Analysis of chimeric reads characterises the diverse targetome of AGO2-mediated regulation

Using Attribution Sequence Alignment to Interpret Deep Learning Models for miRNA Binding Site Prediction