Abstract
miRNAs play a key role in regulation of gene expression. Nowadays it is known more than 2500 human miRNAs, while a majority of miRNA–mRNA interactions remains unidentified. The recent development of a high-throughput CLASH (crosslinking, ligation and sequencing of hybrids) technique for discerning miRNA–mRNA interactions allowed an experimental analysis of the human miRNA–mRNA interactome. Therefore, it allowed us, for the first time, make an experimental analysis of the human miRNA–mRNA interactome as a whole and an evaluation of the quality of most commonly used miRNA prediction tools (TargetScan, PicTar, PITA, RNA22 and miRanda). To estimate efficiency of the miRNA–mRNA prediction tools, we used next parameters: sensitivity, positive predicted value, predictions in different mRNA regions (3' UTR, CDS, 5' UTR), predictions for different types of interactions (5 classes), predictions of “canonical” and “nocanonical” interactions, similarity with the random generated data. The analysis revealed low efficiency of all prediction programs in comparison with the CLASH data in terms of the all examined parameters.
Similar content being viewed by others
Abbreviations
- AGO:
-
argonaute
- CDS:
-
coding DNA sequence
- CLASH:
-
crosslinking, ligation and sequencing of hybrids
- CLIP:
-
UV crosslinking and immunoprecipitation
- iCLIP:
-
individual-nucleotide resolution cross-linking and immunoprecipitation
- HEK293:
-
Human Embryonic Kidney 293 cells
- hg19:
-
human genome version 19
- HITS-CLIP:
-
high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation
- miRNA:
-
microRNA
- PAR-CLIP:
-
photoactivatableribonucleoside-enhanced-immunoprecipitation
- PPV:
-
positive predictive value
- RISC:
-
RNA-induced silencing complex
- UTR:
-
untranslated region
References
Erson A.E., Petty E.M. 2008. MicroRNAs in development and disease. Clin. Genet. 74, 296–306.
Kisseljov F.L. 2014. MicroRNAs and cancer. Mol. Biol. (Moscow). 48 (2), 197–206.
Shepelev M.V., Kalinichenko S.V., Vikhreva P.N., Korobko I.V. 2016. Selection of microRNA for providing tumor specificity of transgene expression in cancer gene therapy. Mol. Biol. (Moscow). 50 (2), 284–291.
Stroynowska-Czerwinska A., Fiszer A., Krzyzosiak W.J. 2014. The panorama of miRNA-mediated mechanisms in mammalian cells. Cell. Mol. Life Sci. 71, 2253–2270
Carthew R.W., Sontheimer E.J. 2009. Origins and mechanisms of miRNAs and siRNAs. Cell. 136, 642–655.
Meshesha M.K., Veksler-Lublinsky I., Isakov O., Reichenstein I., Shomron N., Kedem K., et al. 2012. The microRNA transcriptome of human cytomegalovirus (HCMV). Open Virol. J. 6, 38
Kozomara A., Griffiths-Jones S. 2011. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 39, D152–D157
Bartel D.P. 2009. MicroRNAs: Target recognition and regulatory functions. Cell. 136, 215–233.
Helwak A., Kudla G., Dudnakova T., Tollervey D. 2013. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell. 153, 654–665.
Ørom U.A., Nielsen F.C., Lund A.H. 2008. MicroRNA-10a binds the 5′ UTR of ribosomal protein mRNAs and enhances their translation. Mol. Cell. 30, 460–471.
Duursma A.M., Kedde M., Schrier M., Le Sage C., Agami R. 2008. miR-148 targets human DNMT3b protein coding region. RNA. 14, 872–877.
Tay Y., Zhang J., Thomson A. M., Lim B., Rigoutsos I. 2008. MicroRNAs to Nanog, Oct4 and Sox2 coding regions modulate embryonic stem cell differentiation. Nature. 455, 1124–1128.
Tsai N.P., Lin Y.L., Wei L.N. 2009. MicroRNA mir-346 targets the 5′-untranslated region of receptor-interacting protein 140 (RIP140) mRNA and up-regulates its protein expression. Biochem. J. 424, 411–418.
Lal A., Kim H.H., Abdelmohsen K., et al. 2008. p16 INK4a translation suppressed by miR-24. PLoS One. 3, e1864.
Witkos T.M., Koscianska E., Krzyzosiak W.J. 2011. Practical aspects of microRNA target prediction. Curr. Mol. Med. 11, 93–109.
Qi Y., Li Y., Zhang L., Huang J. 2013. microRNA expression profiling and bioinformatic analysis of dengue virus-infected peripheral blood mononuclear cells. Mol. Med. Rep. 7, 791–798.
Li H., Xie S., Liu X., et al. 2014. Matrine alters microRNA expression profiles in SGC-7901 human gastric cancer cells. Oncol. Rep. 32, 2118–2126.
Baccarini A., Brown B.D. 2010. Monitoring microRNA activity and validating microRNA targets by reporter-based approaches. Methods Mol. Biol. 667, 215–233. doi 10.1007/978-1-60761-811-9_15
Doench J.G., Petersen C.P., Sharp P.A. 2003. siRNAs can function as miRNAs. Genes Dev. 17, 438–442.
Bassett A.R., Azzam G., Wheatley L., et al. 2014. Understanding functional miRNA-target interactions in vivo by site-specific genome engineering. Nat. Commun. 5, 4640. doi 10.1038/ncomms5640
Grimson A., Farh K.K.H., Johnston W.K., et al. 2007. MicroRNA targeting specificity in mammals: Determinants beyond seed pairing. Mol. Cell. 27, 91–105.
German M.A., Luo S., Schroth G., et al. 2009. Construction of Parallel Analysis of RNA Ends (PARE) libraries for the study of cleaved miRNA targets and the RNA degradome. Nat. Protoc. 4, 356–362.
Licatalosi D.D., Mele A., Fak J.J., et al. 2008. HITSCLIP yields genome-wide insights into brain alternative RNA processing. Nature. 456, 464–469.
Ørom U.A., Lund A.H. 2007. Isolation of microRNA targets using biotinylated synthetic microRNAs. Methods. 43, 162–165.
Agarwal V., Bell G.W., Nam J.W., Bartel D.P. 2015. Predicting effective microRNA target sites in mammalian mRNAs. eLife. 4, e05005. doi 10.7554/eLife.05005
Lall S., Grün D., Krek A., et al. 2006. A genome-wide map of conserved microRNA targets in C. elegans. Curr. Biol. 16, 460–471.
Krek A., Grün D., Poy M.N., et al. 2005. Combinatorial microRNA target predictions. Nat. Genet. 37, 495–500.
Kertesz M., Iovino N., Unnerstall U., et al. 2007. The role of site accessibility in microRNA target recognition. Nat. Genet. 39, 1278–1284.
Miranda K.C., Huynh T., Tay Y., et al. 2006. A patternbased method for the identification of microRNA binding sites and their corresponding heteroduplexes. Cell. 126, 1203–1217.
John B., Enright A.J., Aravin A., et al. 2004. Human microRNA targets. PLoS Biol. 2, e363.
Li J., Liu S., Zhou H., Qu L., Yang J. 2014. starBase v2.0: Decoding miRNA–ceRNA, miRNA–ncRNA and protein–RNA interaction networks from largescale CLIP-seq data. Nucleic Acids Res. 42, D92–D97.
Yang J.H., Li J.H., Shao P., et al. 2011. starBase: A database for exploring microRNA–mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res. 39 (Suppl. 1), D202–D209.
Harrow J., Denoeud F., Frankish A., et al. 2006. GENCODE: Producing a reference annotation for ENCODE. Genome Biol. 7, 1–4.
Harrow J., Frankish A., Gonzalez J.M., et al. 2012. GENCODE: The reference human genome annotation for the ENCODE Project. Genome Res. 22, 1760–1774.
Wang X. 2016. Improving microRNA target prediction by modelling with unambiguously identified microRNA-target pairs from CLIP-ligation studies. Bioinformatics. 32, 1316–1322.
Gumienny R., Zavolan M. 2015. Accurate transcriptome-wide prediction of microRNA targets and small interfering RNA off-targets with MIRZA-G. Nucleic Acids Res. 43, 1380–1391.
Lu Y., Leslie C.S. 2016. Learning to predict miRNA–mRNA interactions from AGO CLIP sequencing and CLASH data. PLoS Comput. Biol. 12, e1005026.
Author information
Authors and Affiliations
Corresponding author
Additional information
Original Russian Text © O.M. Plotnikova, M.Y. Skoblov, 2018, published in Molekulyarnaya Biologiya, 2018, Vol. 52, No. 3, pp. 543–554.
The article was translated by the authors
Rights and permissions
About this article
Cite this article
Plotnikova, O.M., Skoblov, M.Y. Efficiency of the miRNA–mRNA Interaction Prediction Programs. Mol Biol 52, 467–477 (2018). https://doi.org/10.1134/S0026893318020103
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0026893318020103