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Open Access Highly Accessed Methodology article

Identification of microRNA-mRNA modules using microarray data

Vivek Jayaswal12*, Mark Lutherborrow34, David DF Ma34 and Yee H Yang12

Author Affiliations

1 School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia

2 Centre for Mathematical Biology, University of Sydney, Sydney, NSW, Australia

3 Blood Stem Cell and Cancer Research Unit, Department of Haematology, St Vincent Centre for Applied Biomedical Research, Darlinghurst, NSW, Australia

4 Faculty of Medicine, University of New South Wales, Kensington, NSW, Australia

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BMC Genomics 2011, 12:138  doi:10.1186/1471-2164-12-138

Published: 6 March 2011

Abstract

Background

MicroRNAs (miRNAs) are post-transcriptional regulators of mRNA expression and are involved in numerous cellular processes. Consequently, miRNAs are an important component of gene regulatory networks and an improved understanding of miRNAs will further our knowledge of these networks. There is a many-to-many relationship between miRNAs and mRNAs because a single miRNA targets multiple mRNAs and a single mRNA is targeted by multiple miRNAs. However, most of the current methods for the identification of regulatory miRNAs and their target mRNAs ignore this biological observation and focus on miRNA-mRNA pairs.

Results

We propose a two-step method for the identification of many-to-many relationships between miRNAs and mRNAs. In the first step, we obtain miRNA and mRNA clusters using a combination of miRNA-target mRNA prediction algorithms and microarray expression data. In the second step, we determine the associations between miRNA clusters and mRNA clusters based on changes in miRNA and mRNA expression profiles. We consider the miRNA-mRNA clusters with statistically significant associations to be potentially regulatory and, therefore, of biological interest.

Conclusions

Our method reduces the interactions between several hundred miRNAs and several thousand mRNAs to a few miRNA-mRNA groups, thereby facilitating a more meaningful biological analysis and a more targeted experimental validation.