Detecting microRNA activity from gene expression data.

Hdl Handle:
http://hdl.handle.net/10147/114359
Title:
Detecting microRNA activity from gene expression data.
Authors:
Madden, Stephen F; Carpenter, Susan B; Jeffery, Ian B; Björkbacka, Harry; Fitzgerald, Katherine A; O'Neill, Luke A; Higgins, Desmond G
Affiliation:
School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland.
Citation:
Detecting microRNA activity from gene expression data. 2010, 11:257 BMC Bioinformatics
Journal:
BMC bioinformatics
Issue Date:
2010
URI:
http://hdl.handle.net/10147/114359
DOI:
10.1186/1471-2105-11-257
PubMed ID:
20482775
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:
Article
Language:
en
Keywords:
GENETICS
MeSH:
Animals; Databases, Genetic; Gene Expression; Genomics; Humans; Mice; MicroRNAs; Oligonucleotide Array Sequence Analysis; RNA, Messenger
ISSN:
1471-2105

Full metadata record

DC FieldValue Language
dc.contributor.authorMadden, Stephen Fen
dc.contributor.authorCarpenter, Susan Ben
dc.contributor.authorJeffery, Ian Ben
dc.contributor.authorBjörkbacka, Harryen
dc.contributor.authorFitzgerald, Katherine Aen
dc.contributor.authorO'Neill, Luke Aen
dc.contributor.authorHiggins, Desmond Gen
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
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