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mirTarPri: Improved Prioritization of MicroRNA Targets through Incorporation of Functional Genomics Data

Author

Listed:
  • Peng Wang
  • Shangwei Ning
  • Qianghu Wang
  • Ronghong Li
  • Jingrun Ye
  • Zuxianglan Zhao
  • Yan Li
  • Teng Huang
  • Xia Li

Abstract

MicroRNAs (miRNAs) are a class of small (19–25 nt) non-coding RNAs. This important class of gene regulator downregulates gene expression through sequence-specific binding to the 3′untranslated regions (3′UTRs) of target mRNAs. Several computational target prediction approaches have been developed for predicting miRNA targets. However, the predicted target lists often have high false positive rates. To construct a workable target list for subsequent experimental studies, we need novel approaches to properly rank the candidate targets from traditional methods. We performed a systematic analysis of experimentally validated miRNA targets using functional genomics data, and found significant functional associations between genes that were targeted by the same miRNA. Based on this finding, we developed a miRNA target prioritization method named mirTarPri to rank the predicted target lists from commonly used target prediction methods. Leave-one-out cross validation has proved to be successful in identifying known targets, achieving an AUC score up to 0. 84. Validation in high-throughput data proved that mirTarPri was an unbiased method. Applying mirTarPri to prioritize results of six commonly used target prediction methods allowed us to find more positive targets at the top of the prioritized candidate list. In comparison with other methods, mirTarPri had an outstanding performance in gold standard and CLIP data. mirTarPri was a valuable method to improve the efficacy of current miRNA target prediction methods. We have also developed a web-based server for implementing mirTarPri method, which is freely accessible at http://bioinfo.hrbmu.edu.cn/mirTarPri.

Suggested Citation

  • Peng Wang & Shangwei Ning & Qianghu Wang & Ronghong Li & Jingrun Ye & Zuxianglan Zhao & Yan Li & Teng Huang & Xia Li, 2013. "mirTarPri: Improved Prioritization of MicroRNA Targets through Incorporation of Functional Genomics Data," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-12, January.
  • Handle: RePEc:plo:pone00:0053685
    DOI: 10.1371/journal.pone.0053685
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    References listed on IDEAS

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    1. Catia Pesquita & Daniel Faria & André O Falcão & Phillip Lord & Francisco M Couto, 2009. "Semantic Similarity in Biomedical Ontologies," PLOS Computational Biology, Public Library of Science, vol. 5(7), pages 1-12, July.
    2. Matthias Selbach & Björn Schwanhäusser & Nadine Thierfelder & Zhuo Fang & Raya Khanin & Nikolaus Rajewsky, 2008. "Widespread changes in protein synthesis induced by microRNAs," Nature, Nature, vol. 455(7209), pages 58-63, September.
    3. Stephan Artmann & Klaus Jung & Annalen Bleckmann & Tim Beißbarth, 2012. "Detection of Simultaneous Group Effects in MicroRNA Expression and Related Target Gene Sets," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-11, June.
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