Detecting microRNA activity from gene expression data

Hdl Handle:
http://hdl.handle.net/10147/134618
Title:
Detecting microRNA activity from gene expression data
Authors:
Madden, Stephen F; Carpenter, Susan B; Jeffery, Ian B; Bjorkbacka, Harry; Fitzgerald, Katherine A; O'Neill, Luke A; Higgins, Desmond G
Citation:
BMC Bioinformatics. 2010 May 18;11(1):257
Issue Date:
18-May-2010
URI:
http://hdl.handle.net/10147/134618
Abstract:
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.
Item Type:
Journal Article

Full metadata record

DC FieldValue Language
dc.contributor.authorMadden, Stephen F-
dc.contributor.authorCarpenter, Susan B-
dc.contributor.authorJeffery, Ian B-
dc.contributor.authorBjorkbacka, Harry-
dc.contributor.authorFitzgerald, Katherine A-
dc.contributor.authorO'Neill, Luke A-
dc.contributor.authorHiggins, Desmond G-
dc.date.accessioned2011-06-27T14:17:31Z-
dc.date.available2011-06-27T14:17:31Z-
dc.date.issued2010-05-18-
dc.identifierhttp://dx.doi.org/10.1186/1471-2105-11-257-
dc.identifier.citationBMC Bioinformatics. 2010 May 18;11(1):257-
dc.identifier.urihttp://hdl.handle.net/10147/134618-
dc.description.abstractAbstract 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.-
dc.titleDetecting microRNA activity from gene expression data-
dc.typeJournal Article-
dc.language.rfc3066en-
dc.rights.holderMadden et al.; licensee BioMed Central Ltd.-
dc.description.statusPeer Reviewed-
dc.date.updated2011-06-23T14:06:08Z-
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