Nucleic Acids Research Advance Access originally published online on March 18, 2009
Nucleic Acids Research 2009 37(8):e60; doi:10.1093/nar/gkp153
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Nucleic Acids Research, 2009, Vol. 37, No. 8 e60
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Methods Online |
Identification of microRNAs with regulatory potential using a matched microRNA-mRNA time-course data
1School of Mathematics and Statistics, 2Sydney Bioinformatics, Centre for Mathematical Biology, University of Sydney, Sydney, 3Blood Stem Cell and Cancer Research Unit, St Vincent Centre for Applied Biomedical Research, Department of Haematology, St Vincent's Hospital and St Vincent's Clinical School, University of New South Wales, Darlinghurst, NSW, Australia
*To whom correspondence should be addressed. Tel: +61 2 9351 5794; Fax: +61 2 9351 4534; Email: vivek{at}maths.usyd.edu.au
Received December 18, 2008. Revised February 11, 2009. Accepted February 24, 2009.
Over the past decade, a class of small RNA molecules called microRNAs (miRNAs) has been shown to regulate gene expression at the post-transcription stage. While early work focused on the identification of miRNAs using a combination of experimental and computational techniques, subsequent studies have focused on identification of miRNA-target mRNA pairs as each miRNA can have hundreds of mRNA targets. The experimental validation of some miRNAs as oncogenic has provided further motivation for research in this area. In this article we propose an odds-ratio (OR) statistic for identification of regulatory miRNAs. It is based on integrative analysis of matched miRNA and mRNA time-course microarray data. The OR-statistic was used for (i) identification of miRNAs with regulatory potential, (ii) identification of miRNA-target mRNA pairs and (iii) identification of time lags between changes in miRNA expression and those of its target mRNAs. We applied the OR-statistic to a cancer data set and identified a small set of miRNAs that were negatively correlated to mRNAs. A literature survey revealed that some of the miRNAs that were predicted to be regulatory, were indeed oncogenic or tumor suppressors. Finally, some of the predicted miRNA targets have been shown to be experimentally valid.