Predicting effective microRNA target sites in mammalian mRNAs

By Vikram Agarwal, George W. Bell, Jin-Wu Nam, David P. Bartel. 2015

6,545 citations recorded by the source corpus.

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. DOI: http://dx.doi.org/10.7554/eLife.05005.001

Subfields: Cancer Research, Molecular Biology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “For instance, one of the most widely adopted tools for miRNA target prediction, TargetScan, trained multiple linear regression models on 14 hand-curated features including structural accessibility of the target site on the mRNA, the degree of site conservation, and predicted thermodynamic stability ”
Global Mapping of Small RNA-Target Interactions in Bacteria (2016, positive): “A similar observation was reported recently for miRNA targets revealed by the CLASH methodology (Helwak et al., 2013; Agarwal et al., 2015).”
Metazoan MicroRNAs (2018, background): “However, the noncanonical binding sites identified by crosslinking do not function to mediate detectable repression, in that mRNAs with only these sites are no more responsive to miRNA perturbation than are other mRNAs without canonical sites (Agarwal et al., 2015).”
Tools for Sequence-Based miRNA Target Prediction: What to Choose? (2016, methodology): “In order to relate the 14 features, multiple linear regression models were applied to each of the four site types (8mer, 7mer-m8, 7mer-A1, and 6mer) and collectively called the “context++” model of miRNA targeting efficacy [89].”
UALCAN: An update to the integrated cancer data analysis platform (2022, methodology): “This effort includes miRNA target databases such as TargetScan, comprised of target predictions based on conserved sites in miRNA seed regions [17] ; microRNA.org, in which target predictions are based on a miRanda algorithm [18] ; and miRDB, which includes miRNA targets predicted by the MirTarget…”
MIENTURNET: an interactive web tool for microRNA-target enrichment and network-based analysis (2019, methodology): “We generated simulated expression profiles for 100 samples spanning 12440 genes that are available in Tar-getscan (Release 7.2) [11] and for 100 samples spanning 14888 genes that are available in miRTarBase (Release 7.0, September 2017) [24].”
The biochemical basis of microRNA targeting efficacy (2018, methodology): “14) thatwere also tested on changes inmRNA levels observed in response to miRNA transfection (14).”
Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer (2020, methodology): “As we expected and initially speculated on obtaining the interpretable results, top ranked miRNAs were matched to the sequences of top ranked genes analyzed by TargetScanHuman web server [23] (http://www.”
miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database (2015, neutral): “0) (41) for target site identification.”
Gene co-expression analysis for functional classification and gene–disease predictions (2017, background): “Finally, miRNA–target interactions can be identified using several in silico target prediction tools [158, 159] or using manually curated databases of experimentally supported target interactions [160–162].”