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- Title
Detecting microRNA activity from gene expressiondata.
- Authors
Madden, Stephen F.; Carpenter, Susan B.; Jeffery, Ian B.; Björkbacka, Harry; Fitzgerald, Katherine A.; O'Neill, Luke A.; Higgins, Desmond G.
- 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.
- Subjects
RNA; GENE expression; PROTEINS; BIOMOLECULES; BIOINFORMATICS
- Publication
BMC Bioinformatics, 2010, Vol 11, p257
- ISSN
1471-2105
- Publication type
Article
- DOI
10.1186/1471-2105-11-257