ArticleeLife2015
Predicting effective microRNA target sites in mammalian mRNAs.
Article in eLife, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4,517 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4,517 citing papers in PubMed.
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- miR-196a reduces mutant Huntingtin aggregates by Rad23b-mediated degradation in Huntington's disease.Journal of biomedical science · 2026Article
- Allele-Specific MicroRNA-Binding Variants at Colorectal Cancer Risk Loci in a Hispanic/Latino Population: An Integrative GWAS, All of Us, and Epigenomic Analysis.medRxiv : the preprint server for health sciences · 2026Article
- Mapping the MicroRNA Landscape of Cadmium Exposure: A Bibliometric and Bioinformatics Analysis of Molecular Targets and Regulatory Pathways.International journal of molecular sciences · 2026Review
- Potential Mechanisms Linking Excessive Testosterone to PMOS: Insights from Network Toxicology and Machine Learning.Metabolites · 2026Article
- DNA/RNA Hybrid Hairpin Gold Nanobeacons Targeting miR-31 Reprogram Invasion in Lung Cancer and Remodel Tumor Histo-ArchitectureACS materials Au · 2026Article
4,457 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.