Detecting microRNA activity from gene expression data.
dc.contributor.author | Madden, Stephen F | |
dc.contributor.author | Carpenter, Susan B | |
dc.contributor.author | Jeffery, Ian B | |
dc.contributor.author | Björkbacka, Harry | |
dc.contributor.author | Fitzgerald, Katherine A | |
dc.contributor.author | O'Neill, Luke A | |
dc.contributor.author | Higgins, Desmond G | |
dc.date.accessioned | 2010-11-02T15:46:46Z | |
dc.date.available | 2010-11-02T15:46:46Z | |
dc.date.issued | 2010 | |
dc.identifier.citation | Detecting microRNA activity from gene expression data. 2010, 11:257 BMC Bioinformatics | en |
dc.identifier.issn | 1471-2105 | |
dc.identifier.pmid | 20482775 | |
dc.identifier.doi | 10.1186/1471-2105-11-257 | |
dc.identifier.uri | http://hdl.handle.net/10147/114359 | |
dc.description.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. | |
dc.language.iso | en | en |
dc.subject | GENETICS | en |
dc.subject.mesh | Animals | |
dc.subject.mesh | Databases, Genetic | |
dc.subject.mesh | Gene Expression | |
dc.subject.mesh | Genomics | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Mice | |
dc.subject.mesh | MicroRNAs | |
dc.subject.mesh | Oligonucleotide Array Sequence Analysis | |
dc.subject.mesh | RNA, Messenger | |
dc.title | Detecting microRNA activity from gene expression data. | en |
dc.type | Article | en |
dc.contributor.department | School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland. | en |
dc.identifier.journal | BMC bioinformatics | en |
refterms.dateFOA | 2018-08-22T09:44:35Z | |
html.description.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. |