Title
Detecting microRNA activity from gene expression data.
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.
Year
DOI
Venue
2010
10.1186/1471-2105-11-257
BMC Bioinformatics
Keywords
Field
DocType
gene expression,bioinformatics,microarray data,micrornas,correspondence analysis,algorithms,non coding rna,microarrays,genetics,messenger rna,microrna,genomics,biochemistry
Gene knockdown,Gene silencing,Biology,Post-transcriptional regulation,Gene expression,Gene product,Regulation of gene expression,Regulator gene,Bioinformatics,Genetics,Lin-4 microRNA precursor
Journal
Volume
Issue
ISSN
11
1
1471-2105
Citations 
PageRank 
References 
15
0.90
10
Authors
7