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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1658049
- author
- Madden, Stephen F.
; Carpenter, Susan B.
; Jeffery, Ian B.
; Björkbacka, Harry
LU
; Fitzgerald, Katherine A.
; O'Neill, Luke A.
and Higgins, Desmond G.
- organization
- publishing date
- 2010
- type
- Contribution to journal
- publication status
- published
- subject
- in
- BMC Bioinformatics
- volume
- 11
- publisher
- BioMed Central (BMC)
- external identifiers
-
- wos:000279730000002
- scopus:77952256307
- pmid:20482775
- 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
- 2016-04-01 14:00:47
- date last changed
- 2022-02-04 18:40:23
@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 (BMC)}},
series = {{BMC Bioinformatics}},
title = {{Detecting microRNA activity from gene expression data}},
url = {{http://dx.doi.org/10.1186/1471-2105-11-257}},
doi = {{10.1186/1471-2105-11-257}},
volume = {{11}},
year = {{2010}},
}