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Open Access Highly Accessed Research article

Detecting microRNA activity from gene expression data

Stephen F Madden13, Susan B Carpenter2, Ian B Jeffery1, Harry Björkbacka4, Katherine A Fitzgerald2, Luke A O'Neill2 and Desmond G Higgins1*

Author Affiliations

1 School of Medicine and Medical Science, Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland

2 School of Biochemistry and Immunology, Trinity College Dublin, Dublin 2, Ireland

3 National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland

4 Department of Clinical Sciences, Malmö University Hospital, Lund University, Malmö, Sweden

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BMC Bioinformatics 2010, 11:257  doi:10.1186/1471-2105-11-257

Published: 18 May 2010

Abstract

Background

MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions.

Results

Here we combine correspondence analysis, between group analysis and co-inertia analysis (CIA) to determine which miRNAs are associated with differences in gene expression levels in microarray data sets. Using a database of miRNA target predictions from TargetScan, TargetScanS, PicTar4way PicTar5way, and miRanda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of miRNAs associated with a specified split in samples. We applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. In each case we were able to identified miRNAs of biological importance.

Conclusions

We describe a technique to integrate gene expression data and miRNA target predictions from multiple sources.