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BMC Bioinformatics. 2010 May 18;11:257. doi: 10.1186/1471-2105-11-257.

Detecting microRNA activity from gene expression data.

Author information

  • 1School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland.

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.

PMID:
20482775
[PubMed - indexed for MEDLINE]
PMCID:
PMC2885376
Free PMC Article

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