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MicroRNA target site identification by integrating sequence and binding information

Nature Methods volume 10, pages 630–633 (2013)Cite this article

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Abstract

High-throughput sequencing has opened numerous possibilities for the identification of regulatory RNA-binding events. Cross-linking and immunoprecipitation of Argonaute proteins can pinpoint a microRNA (miRNA) target site within tens of bases but leaves the identity of the miRNA unresolved. A flexible computational framework, microMUMMIE, integrates sequence with cross-linking features and reliably identifies the miRNA family involved in each binding event. It considerably outperforms sequence-only approaches and quantifies the prevalence of noncanonical binding modes.

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Figure 1: Identifying miRNA target sites by a joint sequence-interaction model.
Figure 2: Validation of predicted sites and their impact on expression.

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Gene Expression Omnibus

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Gene Expression Omnibus

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Acknowledgements

This work was supported by grants from the US National Science Foundation (MCB-0822033) and US National Institutes of Health (NIH R01-GM104962) to U.O. and by awards from the NIH (R01-AI067968; R01-DA030086) to B.R.C. Contributions by R.L.S. were additionally supported by a Duke Center for AIDS Research small grant award (P30-AI064518). We thank S. Grosswendt and M. Piechotta for critical reading of a manuscript draft.

Author information

Author notes

  1. William H Majoros and Parawee Lekprasert: These authors contributed equally to this work.

Authors and Affiliations

  1. Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, USA

    William H Majoros, Parawee Lekprasert, Neelanjan Mukherjee, David L Corcoran & Uwe Ohler

  2. Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, USA

    Parawee Lekprasert

  3. Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany

    Neelanjan Mukherjee & Uwe Ohler

  4. Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina, USA

    Rebecca L Skalsky & Bryan R Cullen

  5. Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA

    Uwe Ohler

  6. Department of Biology, Humboldt University of Berlin, Berlin, Germany

    Uwe Ohler

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  1. William H Majoros
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  4. Rebecca L Skalsky
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Contributions

W.H.M. implemented the modeling framework and designed the models, P.L. and W.H.M. performed the computational experiments, P.L. investigated bulge sites, and N.M. and D.L.C. configured and ran the PARalyzer pipeline and provided guidance on analyzing its outputs. P.L., W.H.M. and U.O. analyzed the data. R.L.S., B.R.C. and N.M. designed and performed wet-lab experiments, and W.H.M., P.L. and U.O. wrote the manuscript.

Corresponding author

Correspondence to Uwe Ohler.

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The authors declare no competing financial interests.

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Supplementary Figures 1–13 and Supplementary Tables 1–4 (PDF 2192 kb)

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Majoros, W., Lekprasert, P., Mukherjee, N. et al. MicroRNA target site identification by integrating sequence and binding information. Nat Methods 10, 630–633 (2013). https://doi.org/10.1038/nmeth.2489

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