Show simple item record

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
refterms.dateFOA2018-08-08T13:20:42Z
html.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.


Files in this item

Thumbnail
Name:
1471-2105-11-257.xml
Size:
138.8Kb
Format:
XML
Thumbnail
Name:
1471-2105-11-257-S1.XLS
Size:
24.5Kb
Format:
Microsoft Excel
Thumbnail
Name:
1471-2105-11-257-S2.DOC
Size:
42Kb
Format:
Microsoft Word
Thumbnail
Name:
1471-2105-11-257-S3.DOC
Size:
49.5Kb
Format:
Microsoft Word
Thumbnail
Name:
1471-2105-11-257.pdf
Size:
1.187Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record