Show simple item record

dc.contributor.authorMadden, Stephen F
dc.contributor.authorCarpenter, Susan B
dc.contributor.authorJeffery, Ian B
dc.contributor.authorBjörkbacka, Harry
dc.contributor.authorFitzgerald, Katherine A
dc.contributor.authorO'Neill, Luke A
dc.contributor.authorHiggins, Desmond G
dc.date.accessioned2010-11-02T15:46:46Z
dc.date.available2010-11-02T15:46:46Z
dc.date.issued2010
dc.identifier.citationDetecting microRNA activity from gene expression data. 2010, 11:257 BMC Bioinformaticsen
dc.identifier.issn1471-2105
dc.identifier.pmid20482775
dc.identifier.doi10.1186/1471-2105-11-257
dc.identifier.urihttp://hdl.handle.net/10147/114359
dc.description.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.
dc.language.isoenen
dc.subjectGENETICSen
dc.subject.meshAnimals
dc.subject.meshDatabases, Genetic
dc.subject.meshGene Expression
dc.subject.meshGenomics
dc.subject.meshHumans
dc.subject.meshMice
dc.subject.meshMicroRNAs
dc.subject.meshOligonucleotide Array Sequence Analysis
dc.subject.meshRNA, Messenger
dc.titleDetecting microRNA activity from gene expression data.en
dc.typeArticleen
dc.contributor.departmentSchool of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland.en
dc.identifier.journalBMC bioinformaticsen
refterms.dateFOA2018-08-22T09:44:35Z
html.description.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.


Files in this item

Thumbnail
Name:
20482775.pdf
Size:
1.187Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record