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Detecting microRNA activity from gene expression data.
Madden, Stephen F Carpenter, Susan B Jeffery, Ian B Björkbacka, Harry Fitzgerald, Katherine A O'Neill, Luke A Higgins, Desmond G
Madden, Stephen F
Carpenter, Susan B
Jeffery, Ian B
Björkbacka, Harry
Fitzgerald, Katherine A
O'Neill, Luke A
Higgins, Desmond G
Advisors
Editors
Other Contributors
Date
2010
Date Submitted
Keywords
GENETICS
Other Subjects
Subject Mesh
Animals
Databases, Genetic
Gene Expression
Genomics
Humans
Mice
MicroRNAs
Oligonucleotide Array Sequence Analysis
RNA, Messenger
Databases, Genetic
Gene Expression
Genomics
Humans
Mice
MicroRNAs
Oligonucleotide Array Sequence Analysis
RNA, Messenger
Planned Date
Start Date
Collaborators
Principal Investigators
Files
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20482775.pdf
Adobe PDF, 1.19 MB
Alternative Titles
Publisher
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.
Language
en
ISSN
1471-2105
eISSN
ISBN
DOI
10.1186/1471-2105-11-257
PMID
20482775