Fast and effective prediction of microRNA/target duplexes
Open accessAbstract
MicroRNAs (miRNAs) are short RNAs that post-transcriptionally regulate the expression of target genes by binding to the target mRNAs. Although a large number of animal miRNAs has been defined, only a few targets are known. In contrast to plant miRNAs, which usually bind nearly perfectly to their targets, animal miRNAs bind less tightly, with a few nucleotides being unbound, thus producing more complex secondary structures of miRNA/target duplexes. Here, we present a program, RNA-hybrid, that predicts multiple potential binding sites of miRNAs in large target RNAs. In general, the program finds the energetically most favorable hybridization sites of a small RNA in a large RNA. Intramolecular hybridizations, that is, base pairings between target nucleotides or between miRNA nucleotides are not allowed. For large targets, the time complexity of the algorithm is linear in the target length, allowing many long targets to be searched in a short time. Statistical significance of predicted targets is assessed with an extreme value statistics of length normalized minimum free energies, a Poisson approximation of multiple binding sites, and the calculation of effective numbers of orthologous targets in comparative studies of multiple organisms. We applied our method to the prediction of Drosophila miRNA targets in 3′UTRs and coding sequence. RNAhybrid, with its accompanying programs RNAcalibrate and RNAeffective, is available for download and as a Web tool on the Bielefeld Bioinformatics Server ( http://bibiserv.techfak.uni-bielefeld.de/rnahybrid/ ).
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
miR-27a/b have multiple putative binding sites in the 3'UTR of human PINK1 mRNA with low binding free energies.
“miR-27a/b have multiple putative binding sites in the 3′UTR of human PINK1 mRNA with low binding free energies (Additional file 2b) [37].”
From miR-27a and miR-27b regulate autophagic clearance of damaged mitochondria by targeting PTEN-induced putative kinase 1 (PINK1) (2016) · cites this paper as [37]
The Introduction and 'Multiple hits per target' section describe RNAhybrid as a method for predicting multiple miRNA binding sites and their free energies. The paper does not report binding sites for miR-27a/b in the human PINK1 3′UTR, so it supports the prediction method but not the specific result attributed here. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRNAhybrid analysis demonstrates extensive pairing between mature miR-181b and a segment on the 5' end of the HMGA1 mRNA.
“While not predicted by Targetscan, RNAhybrid analysis (Rehmsmeier et al., 2004) demonstrates extensive pairing between the mature miR-181b and a segment on the 5’ end of the HMGA1 mRNA.”
From Evidence for miR‐181 involvement in neuroinflammatory responses of astrocytes (2013) · cites this paper as Rehmsmeier et al., 2004
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Mfold web server for nucleic acid folding and hybridization prediction
Zuker, M. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg595 · Cited by 13,404 · see citing works
The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14
Lee, R. C., Feinbaum, R. L., & Ambros, V. (1993) · Cell · doi:10.1016/0092-8674(93)90529-y · Cited by 12,948 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- Giegerich, R. and Steffen, P. 2002. Implementing algebraic dynamic programming in the functional and the imperative programming paradigm. In Mathematics of program construction (eds. E. Boiten and B. Möller), pp. 1-20. Springer LNCS 2386, Heidelberg, Ger- many.
- Gumbel, E.J. 1958. Statistics of extremes. Columbia University Press, New York.
- Karlin, S. and Altschul, S.F. 1990. Methods for assessing the statistical significance of molecular sequence features by using general scor- ing schemes. Proc. Natl. Acad. Sci. 87: 2264-2268.
- Kuzin, A., Stivers, C., Brody, T., and Odenwald, W.F. 2003. nerfin-1, a member of a conserved Zn-finger gene subfamily, is required for proper CNS axon guidance. In A. Dros. Res. Conf. 44, page 710B. The Genetics Society of America, Bethesda, MD.
- Orso, G., Rossetto, M., Sartori, E., and Daga, A. 2003. Functional analysis of the Drosophila spastin gene. In A. Dros. Res. Conf. 44, page 786C. The Genetics Society of America, Bethesda, MD.
- Waterman, M.S. and Vingron, M. 1994. Rapid and accurate estimates of statistical significance for sequence database searches. Proc. Natl. Acad. Sci. 91: 4625-4628.
Cited by
Sorted by citations
Lorenz, R., Bernhart, S. H., Höner zu Siederdissen, C., Tafer, H., Flamm, C., Stadler, P. F., & Hofacker, I. L. (2011) · Algorithms for Molecular Biology · doi:10.1186/1748-7188-6-26 · Cited by 5,098 · see citing works
The role of site accessibility in microRNA target recognition
Kertesz, M., Iovino, N., Unnerstall, U., Gaul, U., & Segal, E. (2007) · Nature Genetics · doi:10.1038/ng2135 · Cited by 2,460 · see citing works
Specific effects of microRNAs on the plant transcriptome
Schwab, R., Palatnik, J. F., Riester, M., Schommer, C., Schmid, M., & Weigel, D. (2005) · Developmental Cell · doi:10.1016/j.devcel.2005.01.018 · Cited by 1,467 · see citing works
Stark, A., Brennecke, J., Bushati, N., Russell, R. B., & Cohen, S. M. (2005) · Cell · doi:10.1016/j.cell.2005.11.023 · Cited by 1,080 · see citing works
MicroRNA 21 Promotes Glioma Invasion by Targeting Matrix Metalloproteinase Regulators
Gabriely, G., Wurdinger, T., Kesari, S., Esau, C. C., Burchard, J., Linsley, P. S., & Krichevsky, A. M. (2008) · Molecular and Cellular Biology · doi:10.1128/mcb.00479-08 · Cited by 888 · see citing works
IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions
Mann, M., Wright, P. R., & Backofen, R. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx279 · Cited by 725 · see citing works
Busch, A., Richter, A. S., & Backofen, R. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn544 · Cited by 552 · see citing works
Identifying miRNAs, targets and functions
Liu, B., Li, J., & Cairns, M. J. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbs075 · Cited by 563 · see citing works
Pairing beyond the Seed Supports MicroRNA Targeting Specificity
Broughton, J. P., Lovci, M. T., Huang, J. L., Yeo, G. W., & Pasquinelli, A. E. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.09.004 · Cited by 487 · see citing works
Dalloul, R. A., Long, J. A., Zimin, A. V., Aslam, L., Beal, K., Ann Blomberg, L., Bouffard, P., Burt, D. W., Crasta, O., Crooijmans, R. P. M. A., et al. (2010) · PLoS Biology · doi:10.1371/journal.pbio.1000475 · Cited by 416 · see citing works
An alternative mode of microRNA target recognition
Chi, S. W., Hannon, G. J., & Darnell, R. B. (2012) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2230 · Cited by 328 · see citing works
MicroRNAs regulate the expression of the alternative splicing factor nPTB during muscle development
Boutz, P. L., Chawla, G., Stoilov, P., & Black, D. L. (2007) · Genes & Development · doi:10.1101/gad.1500707 · Cited by 304 · see citing works
Evidence for miR‐181 involvement in neuroinflammatory responses of astrocytes
Hutchison, E. R., Kawamoto, E. M., Taub, D. D., Lal, A., Abdelmohsen, K., Zhang, Y., Wood, W. H., Lehrmann, E., Camandola, S., Becker, K. G., et al. (2013) · Glia · doi:10.1002/glia.22483 · Cited by 246 · see citing works
MicroRNA-519c Suppresses Hypoxia-Inducible Factor-1α Expression and Tumor Angiogenesis
Cha, S.-T., Chen, P.-S., Johansson, G., Chu, C.-Y., Wang, M.-Y., Jeng, Y.-M., Yu, S.-L., Chen, J.-S., Chang, K.-J., Jee, S.-H., et al. (2010) · Cancer Research · doi:10.1158/0008-5472.can-09-2448 · Cited by 202 · see citing works
Telomere Dysfunction Induces Sirtuin Repression that Drives Telomere-Dependent Disease
Amano, H., Chaudhury, A., Rodriguez-Aguayo, C., Lu, L., Akhanov, V., Catic, A., Popov, Y. V., Verdin, E., Johnson, H., Stossi, F., et al. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.03.001 · Cited by 182 · see citing works
miR-219 Cooperates with miR-338 in Myelination and Promotes Myelin Repair in the CNS
Wang, H., Moyano, A. L., Ma, Z., Deng, Y., Lin, Y., Zhao, C., Zhang, L., Jiang, M., He, X., Ma, Z., et al. (2017) · Developmental Cell · doi:10.1016/j.devcel.2017.03.001 · Cited by 174 · see citing works
Kim, J., Fiesel, F. C., Belmonte, K. C., Hudec, R., Wang, W.-X., Kim, C., Nelson, P. T., Springer, W., & Kim, J. (2016) · Molecular Neurodegeneration · doi:10.1186/s13024-016-0121-4 · Cited by 163 · see citing works
“We first searched miRNAs that have putative binding sites in the 3′UTR of human PINK1 mRNA by utilizing several miRNA-target prediction algorithms, such as miRanda [67], miRWalk [68], RNAhybrid [37], and Targetscan [69].”
BackgroundMethodIdentifying microRNA targets in different gene regions
Xu, W., San Lucas, A., Wang, Z., & Liu, Y. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s7-s4 · Cited by 159 · see citing works
Xu, B., Hsu, P.-K., Stark, K. L., Karayiorgou, M., & Gogos, J. A. (2013) · Cell · doi:10.1016/j.cell.2012.11.052 · Cited by 145 · see citing works
Intronic miR-26b controls neuronal differentiation by repressing its host transcript,<i>ctdsp2</i>
Dill, H., Linder, B., Fehr, A., & Fischer, U. (2012) · Genes & Development · doi:10.1101/gad.177774.111 · Cited by 125 · see citing works
miR-323a regulates ERBB4 and is involved in depression
Fiori, L. M., Kos, A., Lin, R., Théroux, J.-F., Lopez, J. P., Kühne, C., Eggert, C., Holzapfel, M., Huettl, R.-E., Mechawar, N., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-020-00953-7 · Cited by 88 · see citing works
Ghose, J., Sinha, M., Das, E., Jana, N. R., & Bhattacharyya, N. P. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023837 · Cited by 98 · see citing works
Böhlig, L., Friedrich, M., & Engeland, K. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq796 · Cited by 85 · see citing works
Deconstructing Stepwise Fate Conversion of Human Fibroblasts to Neurons by MicroRNAs
Cates, K., McCoy, M. J., Kwon, J.-S., Liu, Y., Abernathy, D. G., Zhang, B., Liu, S., Gontarz, P., Kim, W. K., Chen, S., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2020.08.015 · Cited by 67 · see citing works
Shi, S., Leites, C., He, D., Schwartz, D., Moy, W., Shi, J., & Duan, J. (2014) · Journal of Biological Chemistry · doi:10.1074/jbc.m113.535203 · Cited by 61 · see citing works
MicroRNA Expression in the Glaucomatous Retina
Jayaram, H., Cepurna, W. O., Johnson, E. C., & Morrison, J. C. (2015) · Investigative Opthalmology & Visual Science · doi:10.1167/iovs.15-18088 · Cited by 60 · see citing works
Zheng, Y., He, J., Guo, L., Yao, L., Zheng, X., Yang, Z., Xia, Y., Wu, X., Su, Y., Xu, N., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00314 · Cited by 52 · see citing works
Bortezomib Sustains T Cell Function by Inducing miR-155-Mediated Downregulation of SOCS1 and SHIP1
Renrick, A. N., Thounaojam, M. C., de Aquino, M. T. P., Chaudhuri, E., Pandhare, J., Dash, C., & Shanker, A. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.607044 · Cited by 43 · see citing works
Tian, W.-H., Wang, Z., Yue, Y.-X., Li, H., Li, Z.-J., Han, R.-L., Tian, Y.-D., Kang, X.-T., & Liu, X.-J. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184420 · Cited by 35 · see citing works
A MIR4646 associated methylation locus is hypomethylated in adolescent depression
Boström, A. E., Ciuculete, D.-M., Attwood, M., Krattinger, R., Nikontovic, L., Titova, O. E., Kullak-Ublick, G. A., Mwinyi, J., & Schiöth, H. B. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.05.017 · Cited by 33 · see citing works
The role of miR-106p-5p in cervical cancer: from expression to molecular mechanism
Yi, Y., Liu, Y., Wu, W., Wu, K., & Zhang, W. (2018) · Cell Death Discovery · doi:10.1038/s41420-018-0096-8 · Cited by 23 · see citing works
Bjerke, G. A., & Yi, R. (2020) · RNA · doi:10.1261/rna.073635.119 · Cited by 22 · see citing works
Deciphering synergistic regulatory networks of microRNAs in hESCs and fibroblasts
Sahu, M., & Mallick, B. (2018) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2018.03.061 · Cited by 16 · see citing works
Functional interaction between miR-505-3p and miR-21 contributes to GBM suppression
Banadkooki, M. Z., Soltani, B. M., & Mowla, S. J. (2026) · Scientific Reports · doi:10.1038/s41598-026-59926-5
“A web-based RNA hybrid tool examined Pri miR-21 and miR-505-3p sequences 25 .”
- Authors
- REHMSMEIER, M., STEFFEN, P., HÖCHSMANN, M., & GIEGERICH, R.
- Affiliations
- Bielefeld University (DE)
- ORCID
- REHMSMEIER, MARC
- Venue
- RNA
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2004-09-21
- Volume
- 10
- Issue
- 10
- Pages
- 1507-1517
- ISSN
- 1355-8382 (ISSN-L)
- 1469-9001
- PMC
- PMC1370637
- OpenAlex
- W2094253263
- Cited by
- 2,560
- References
- 2
- Citations by year
- 2026: 56 · 2025: 114 · 2024: 141 · 2023: 130 · 2022: 156 · 2021: 158 · 2020: 171 · 2019: 150 · 2018: 134 · 2017: 140 · 2016: 141 · 2015: 132 · 2014: 155 · 2013: 141 · 2012: 135
- FWCI
- 21.04
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- RNA (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
- Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Keywords
- miRNA target prediction, RNAhybrid, RNA hybridization, post-transcriptional regulation, 3' untranslated region, RNA secondary structure, minimum free energy, extreme value statistics, Poisson approximation, comparative genomics
- MeSH terms
- Algorithms, Animals, Binding Sites, Drosophila, Nucleic Acid Conformation, RNA Processing, Post-Transcriptional, RNA, Messenger, Thermodynamics, Genes, Insect, Computational Biology, Body Patterning, 3' Untranslated Regions, Databases, Nucleic Acid, MicroRNAs
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review