AuthorsMadden, Stephen F
Carpenter, Susan B
Jeffery, Ian B
Fitzgerald, Katherine A
O'Neill, Luke A
Higgins, Desmond G
AffiliationSchool of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland.
Oligonucleotide Array Sequence Analysis
MetadataShow full item record
CitationDetecting microRNA activity from gene expression data. 2010, 11:257 BMC Bioinformatics
AbstractBACKGROUND: 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.
- Correlation of expression profiles between microRNAs and mRNA targets using NCI-60 data.
- Authors: Wang YP, Li KB
- Issue date: 2009 May 12
- Bioinformatics Resource Manager v2.3: an integrated software environment for systems biology with microRNA and cross-species analysis tools.
- Authors: Tilton SC, Tal TL, Scroggins SM, Franzosa JA, Peterson ES, Tanguay RL, Waters KM
- Issue date: 2012 Nov 23
- Modeling microRNA-mRNA interactions using PLS regression in human colon cancer.
- Authors: Li X, Gill R, Cooper NG, Yoo JK, Datta S
- Issue date: 2011 May 19
- Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers.
- Authors: Peng X, Li Y, Walters KA, Rosenzweig ER, Lederer SL, Aicher LD, Proll S, Katze MG
- Issue date: 2009 Aug 11
- Increasing MicroRNA target prediction confidence by the relative R(2) method.
- Authors: Wang H, Li WH
- Issue date: 2009 Aug 21