The limits of human microRNA annotation have been met
Abstract
Over the last few years, the number of microRNAs in the human genome has become a controversially debated issue. Several publications reported thousands of putative novel microRNAs not included in the curated microRNA gene database MirGeneDB and the repository miRBase. Recently, by using sequencing of ∼300 human tissues and cell lines, the human RNA atlas, an expanded inventory of human RNA annotations, was published, reporting thousands of putative microRNAs. We, the developers of established microRNA prediction tools and hosts of MirGeneDB, raise concerns about the frequently applied prediction and functional validation strategies, briefly discussing the drawbacks of false positive detections. By means of quantifying well-established biogenesis-derived features, we show that the reported novel microRNAs essentially represent false-positives and argue that the human microRNA complement, at about 550 microRNA genes, is already near complete. Output of available tools must be curated as false predictions will misguide scientists looking for biomarkers or therapeutic targets.
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We have high confidence in the validity of the miRNAs we did consider, which improves the signal in our observations and increases the confidence that our results provide a solid foundation to build on in future downstream analyses.
“The trade-off for this choice is that we have high confidence in the validity of the miRNAs we did consider, which improves the signal in our observations and increases the confidence that our results provide a solid foundation to build on in future downstream analyses11.”
From Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions (2024) · cites this paper as [11]
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Londin, E., Loher, P., Telonis, A. G., Quann, K., Clark, P., Jing, Y., Hatzimichael, E., Kirino, Y., Honda, S., Lally, M., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1420955112 · Cited by 404 · see citing works
Reexamining assumptions about miRNA-guided gene silencing
Kilikevicius, A., Meister, G., & Corey, D. R. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1256 · Cited by 194 · see citing works
Flagged first, then by citations
Research works printed but not linked: 30
- Alles J, Fehlmann T, Fischer U, Backes C, Galata V, Minet M, Hart M, Abu-Halima M, Grässer FA, Lenhof H-P, 2019. An estimate of the total number of true human miRNAs. Nucleic Acids Res 47: 3353– 3364. 10.1093/nar/gkz097 30820533 PMC6468295 · DOI · Paper page
- Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, Chen X, Dreyfuss G, Eddy SR, Griffiths-Jones S, Marshall M, 2003. A uniform system for microRNA annotation. RNA 9: 277– 279. 10.1261/rna.2183803 12592000 PMC1370393 · DOI
- Backes C, Fehlmann T, Kern F, Kehl T, Lenhof H-P, Meese E, Keller A. 2018. miRCarta: a central repository for collecting miRNA candidates. Nucleic Acids Res 46: D160– D167. 10.1093/nar/gkx851 29036653 PMC5753177 · DOI
- Bartel DP. 2018. Metazoan microRNAs. Cell 173: 20– 51. 10.1016/j.cell.2018.03.006 29570994 PMC6091663 · DOI · Paper page
- Blanco-Domínguez R, Sánchez-Díaz R, de la Fuente H, Jiménez-Borreguero LJ, Matesanz-Marín A, Relaño M, Jiménez-Alejandre R, Linillos-Pradillo B, Tsilingiri K, Martín-Mariscal ML, 2021. A novel circulating microRNA for the detection of acute myocarditis. N Engl J Med 384: 2014– 2027. 10.1056/NEJMoa2003608 34042389 PMC8258773 · DOI · Paper page
- Chiang HR, Schoenfeld LW, Ruby JG, Auyeung VC, Spies N, Baek D, Johnston WK, Russ C, Luo S, Babiarz JE, 2010. Mammalian microRNAs: experimental evaluation of novel and previously annotated genes. Genes Dev 24: 992– 1009. 10.1101/gad.1884710 20413612 PMC2867214 · DOI · Paper page
- Chinnappa K, Cárdenas A, Prieto-Colomina A, Villalba A, Márquez-Galera Á, Soler R, Nomura Y, Llorens E, Tomasello U, López-Atalaya JP, 2022. Secondary loss of miR-3607 reduced cortical progenitor amplification during rodent evolution. Sci Adv 8: eabj4010. 10.1126/sciadv.abj4010 35020425 PMC8754304 · DOI
- Friedländer MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N. 2008. Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26: 407– 415. 10.1038/nbt1394 18392026 · DOI
- Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N. 2012. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 40: 37– 52. 10.1093/nar/gkr688 21911355 PMC3245920 · DOI · Paper page
- Fromm B, Billipp T, Peck LE, Johansen M, Tarver JE, King BL, Newcomb JM, Sempere LF, Flatmark K, Hovig E, 2015. A uniform system for the annotation of vertebrate microRNA genes and the evolution of the human microRNAome. Annu Rev Genet 49: 213– 242. 10.1146/annurev-genet-120213-092023 26473382 PMC4743252 · DOI
- Fromm B, Domanska D, Høye E, Ovchinnikov V, Kang W, Aparicio-Puerta E, Johansen M, Flatmark K, Mathelier A, Hovig E, 2020. MirGeneDB 2.0: the metazoan microRNA complement. Nucleic Acids Res 48: D132– D141. 10.1093/nar/gkz885 31598695 PMC6943042 · DOI · Paper page
- Fromm B, Høye E, Domanska D, Zhong X, Aparicio-Puerta E, Ovchinnikov V, Umu SU, Chabot PJ, Kang W, Aslanzadeh M, 2022. MirGeneDB 2.1: toward a complete sampling of all major animal phyla. Nucleic Acids Res 50: D204– D210. 10.1093/nar/gkab1101 34850127 PMC8728216 · DOI · Paper page
- Garcia-Martin R, Wang G, Brandão BB, Zanotto TM, Shah S, Kumar Patel S, Schilling B, Kahn CR. 2022. MicroRNA sequence codes for small extracellular vesicle release and cellular retention. Nature 601: 446– 451. 10.1038/s41586-021-04234-3 34937935 PMC9035265 · DOI
- Hackenberg M, Sturm M, Langenberger D, Falcon-Perez JM, Aransay AM. 2009. miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res 37: W68– W76. 10.1093/nar/gkp347 19433510 PMC2703919 · DOI
- Hamaguchi Y, Zeng C, Hamada M. 2021. Impact of human gene annotations on RNA-seq differential expression analysis. BMC Genomics 22: 730. 10.1186/s12864-021-08038-7 34625021 PMC8501603 · DOI
- Huberdeau MQ, Simard MJ. 2019. A guide to microRNA-mediated gene silencing. FEBS J 286: 642– 652. 10.1111/febs.14666 30267606 · DOI
- Jha A, Panzade G, Pandey R, Shankar R. 2015. A legion of potential regulatory sRNAs exists beyond the typical microRNAs microcosm. Nucleic Acids Res 43: 8713– 8724. 10.1093/nar/gkv871 26354861 PMC4605316 · DOI
- Kim Y-K, Kim B, Narry Kim V. 2016. Re-evaluation of the roles of DROSHA, Exportin 5, and DICER in microRNA biogenesis. Proc Natl Acad Sci 113: E1881– E1889. 10.1073/pnas.1602532113 26976605 PMC4822641 · DOI
- Kozomara A, Griffiths-Jones S. 2014. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42: D68– D73. 10.1093/nar/gkt1181 24275495 PMC3965103 · DOI · Paper page
- Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T. 2001. Identification of novel genes coding for small expressed RNAs. Science 294: 853– 858. 10.1126/science.1064921 11679670 · DOI · Paper page
- Lau NC, Lim LP, Weinstein EG, Bartel DP. 2001. An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294: 858– 862. 10.1126/science.1065062 11679671 · DOI · Paper page
- Lee RC, Ambros V. 2001. An extensive class of small RNAs in Caenorhabditis elegans. Science 294: 862– 864. 10.1126/science.1065329 11679672 · DOI · Paper page
- Lorenzi L, Chiu H-S, Cobos FA, Gross S, Volders P-J, Cannoodt R, Nuytens J, Vanderheyden K, Anckaert J, Lefever S, 2021. The RNA Atlas expands the catalog of human non-coding RNAs. Nat Biotechnol 39: 1453– 1465. 10.1038/s41587-021-00936-1 34140680 · DOI · Paper page
- Nicolas FE, Hall AE, Csorba T, Turnbull C, Dalmay T. 2012. Biogenesis of Y RNA-derived small RNAs is independent of the microRNA pathway. FEBS Lett 586: 1226– 1230. 10.1016/j.febslet.2012.03.026 22575660 · DOI
- Nielsen MM, Pedersen JS. 2021. miRNA activity inferred from single cell mRNA expression. Sci Rep 11: 9170. 10.1038/s41598-021-88480-5 33911110 PMC8080788 · DOI
- Pasquinelli AE, Reinhart BJ, Slack F, Martindale MQ, Kuroda MI, Maller B, Hayward DC, Ball EE, Degnan B, Müller P, 2000. Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA. Nature 408: 86– 89. 10.1038/35040556 11081512 · DOI
- Patil AH, Halushka MK, Fromm B. 2021. No evidence of paralogous loci or new bona fide microRNAs in telomere to telomere (T2T) genomic data. bioRxiv 10.1101/2021.12.09.471935 · DOI · Paper page
- Tarbier M, Mackowiak SD, Frade J, Catuara-Solarz S, Biryukova I, Gelali E, Menéndez DB, Zapata L, Ossowski S, Bienko M, 2020. Nuclear gene proximity and protein interactions shape transcript covariations in mammalian single cells. Nat Commun 11: 5445. 10.1038/s41467-020-19011-5 33116115 PMC7595044 · DOI
- Wang J, Chen J, Sen S. 2016. MicroRNA as biomarkers and diagnostics. J Cell Physiol 231: 25– 30. 10.1002/jcp.25056 26031493 PMC8776330 · DOI · Paper page
- Witwer KW, Halushka MK. 2016. Toward the promise of microRNAs: enhancing reproducibility and rigor in microRNA research. RNA Biol 13: 1103– 1116. 10.1080/15476286.2016.1236172 27645402 PMC5100345 · DOI · Paper page
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Patil, A. H., Baran, A., Brehm, Z. P., McCall, M. N., & Halushka, M. K. (2022) · GigaScience · doi:10.1093/gigascience/giac083 · Cited by 30 · see citing works
Vattathil, S. M., Gerasimov, E. S., Canon, S. M., Lori, A., Tan, S. S. M., Kim, P. J., Liu, Y., Lai, E. C., Bennett, D. A., Wingo, T. S., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00778-x · Cited by 14 · see citing works
- Authors
- Fromm, B., Zhong, X., Tarbier, M., Friedländer, M. R., & Hackenberg, M.
- Venue
- RNA
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2022-03-02
- Volume
- 28
- Issue
- 6
- Pages
- 781-785
- ISSN
- 1355-8382
- 1469-9001
- PMC
- PMC9074900
- Cited by
- 55
- References
- 2
- Language
- en
- Funders
- Tromsø forskningsstiftelse (20_SG_BF)
- Spanish Government (AGL2017-88702-C2-2-R)
- University of Granada (A-BIO-481-UGR18)
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