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Detecting microRNA activity from gene expression data

Madden, Stephen F.; Carpenter, Susan B.; Jeffery, Ian B.; Björkbacka, Harry LU ; Fitzgerald, Katherine A.; O'Neill, Luke A. and Higgins, Desmond G. (2010) In BMC Bioinformatics 11.
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... (More)
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. (Less)
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organization
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type
Contribution to journal
publication status
published
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in
BMC Bioinformatics
volume
11
publisher
BioMed Central
external identifiers
  • wos:000279730000002
  • scopus:77952256307
ISSN
1471-2105
DOI
10.1186/1471-2105-11-257
language
English
LU publication?
yes
id
06cff4ee-3d24-4c40-8cc2-9c1c86cd690b (old id 1658049)
date added to LUP
2010-08-20 08:31:50
date last changed
2018-05-29 09:25:49
@article{06cff4ee-3d24-4c40-8cc2-9c1c86cd690b,
  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.},
  author       = {Madden, Stephen F. and Carpenter, Susan B. and Jeffery, Ian B. and Björkbacka, Harry and Fitzgerald, Katherine A. and O'Neill, Luke A. and Higgins, Desmond G.},
  issn         = {1471-2105},
  language     = {eng},
  publisher    = {BioMed Central},
  series       = {BMC Bioinformatics},
  title        = {Detecting microRNA activity from gene expression data},
  url          = {http://dx.doi.org/10.1186/1471-2105-11-257},
  volume       = {11},
  year         = {2010},
}