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Cited by 7,318MicroRNA in disease regulationRNA Research and SplicingPlant Molecular Biology Research

Predicting effective microRNA target sites in mammalian mRNAs

Open access
Agarwal, Vikram, Bell, George W, Nam, Jin-Wu, Bartel, David P·2015·eLife
DOI 10.7554/elife.05005PMC PMC4532895
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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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Crossref · 10.7554/elife.05005

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  • TargetScan version 7.2 predicts 896 miR-1-3p targets.

    “genes were also gropued based on predictions of 896 miR-1-3p targets and 1,820 miR-124-3p targets by TargetScan version 7.2 (Agarwal et al., 2015)”

    From Scalable and deep profiling of mRNA targets for individual microRNAs with chimeric eCLIP (2022) · cites this paper as Agarwal et al., 2015

    The Results section, “The TargetScan database (v7.0)” (page not provided), says the authors used their model to rank TargetScan version 7 predictions. Our copy does not report version 7.2 or a count of 896 miR-1-3p targets, so it supports the prediction method but not the specific count attributed to it. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • TargetScan version 7.2 predicts 1,820 miR-124-3p targets.

    “genes were also gropued based on predictions of 896 miR-1-3p targets and 1,820 miR-124-3p targets by TargetScan version 7.2 (Agarwal et al., 2015)”

    From Scalable and deep profiling of mRNA targets for individual microRNAs with chimeric eCLIP (2022) · cites this paper as Agarwal et al., 2015

    The Results section, “The TargetScan database (v7.0)” (page not provided), shows predicted miR-124 sites, supporting the use of TargetScan for miR-124 predictions. The Materials and methods section identifies the version studied as 7.0; our copy does not substantiate the claimed version 7.2 or the count of 1,820 targets. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Various algorithms have been developed to predict miRNA:mRNA interactions throughout the transcriptome based on sequence complementarity.

    “Building upon this principle of sequence complementarity, various algorithms have been developed to predict miRNA:mRNA interactions throughout the transcriptome (Agarwal et al., 2015;Krek et al., 2005).”

    From Scalable and deep profiling of mRNA targets for individual microRNAs with chimeric eCLIP (2022) · cites this paper as Agarwal et al., 2015

  • The data clearly demonstrate a need to revise our understanding of the appropriate canonical microRNA sequences for better reproducibility and computational target prediction.

    “Although technical factors may be responsible for some variation between cell types, the data clearly demonstrate a need to revise our understanding of the appropriate canonical microRNA sequences for better reproducibility and computational target prediction (Mestdagh et al. 2014; Agarwal et al. 2015).”

    From Toward the human cellular microRNAome (2017) · cites this paper as Agarwal et al. 2015

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Cited by

Sorted by citations

Authors
Agarwal, V., Bell, G. W., Nam, J.-W., & Bartel, D. P.
Affiliations
  • Howard Hughes Medical Institute (US)
  • Whitehead Institute for Biomedical Research (US)
Corresponding author
Bartel, David P
ORCID
Venue
eLife
Publisher
eLife Sciences Publications, Ltd
Published
2015-08-12
Volume
4
ISSN
2050-084X (ISSN-L)
DOI
10.7554/elife.05005
PMC
PMC4532895
OpenAlex
W2134629862
Cited by
7,318
References
16
Citations by year
2026: 347 · 2025: 499 · 2024: 610 · 2023: 696 · 2022: 1005 · 2021: 1144 · 2020: 959 · 2019: 751 · 2018: 568 · 2017: 447 · 2016: 239 · 2015: 27 · 2014: 3 · 2013: 1 · 2012: 2
FWCI
141.43
Type
journal article
Language
en
Open access
Licence
Locations
  • eLife (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
  • The Royal Society of Chemistry’s Journals, Books and Databases (The Royal Society of Chemistry) (submittedVersion, cc-by)
Funders
  • National Institutes of Health (NIH) (GM067031)
  • Howard Hughes Medical Institute (HHMI) (Investigator)
  • National Science Foundation (NSF) (Graduate Research Fellowship)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Molecular Biology Research
Keywords
microRNA binding sites, miRNA seed region, mRNA repression, TargetScan, quantitative prediction model, gene regulatory networks, UV crosslinking
MeSH terms
Animals, Binding Sites, Gene Expression Regulation, Mammals, Molecular Biology, RNA, Messenger, Computational Biology, MicroRNAs

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