Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/100708
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Type: Journal article
Title: Inferring condition-specific miRNA activity from matched miRNA and mRNA expression data
Author: Zhang, J.
Thuc, D.
Liu, L.
Liu, B.
He, J.
Goodall, G.
Li, J.
Citation: Bioinformatics, 2014; 30(21):3070-3077
Publisher: Oxford University Press
Issue Date: 2014
ISSN: 1367-4803
1460-2059
Statement of
Responsibility: 
Junpeng Zhang, Thuc Duy Le, Lin Liu, Bing Liu, Jianfeng He, Gregory J. Goodall, and Jiuyong Li
Abstract: Motivation: MicroRNAs (miRNAs) play crucial roles in complex cellular networks by binding to the messenger RNAs (mRNAs) of protein coding genes. It has been found that miRNA regulation is often condition-specific. A number of computational approaches have been developed to identify miRNA activity specific to a condition of interest using gene expression data. However, most of the methods only use the data in a single condition, and thus, the activity discovered may not be unique to the condition of interest. Additionally, these methods are based on statistical associations between the gene expression levels of miRNAs and mRNAs, so they may not be able to reveal real gene regulatory relationships, which are causal relationships. Results: We propose a novel method to infer condition-specific miRNA activity by considering (i) the difference between the regulatory behavior that an miRNA has in the condition of interest and its behavior in the other conditions; (ii) the causal semantics of miRNA-mRNA relationships. The method is applied to the epithelial-mesenchymal transition (EMT) and multi-class cancer (MCC) datasets. The validation by the results of transfection experiments shows that our approach is effective in discovering significant miRNA-mRNA interactions. Functional and pathway analysis and literature validation indicate that the identified active miRNAs are closely associated with the specific biological processes, diseases and pathways. More detailed analysis of the activity of the active miRNAs implies that some active miRNAs show different regulation types in different conditions, but some have the same regulation types and their activity only differs in different conditions in the strengths of regulation. Availability and implementation: The R and Matlab scripts are in the Supplementary materials.
Keywords: MicroRNAs
Rights: © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
DOI: 10.1093/bioinformatics/btu489
Grant ID: http://purl.org/au-research/grants/arc/DP130104090
Appears in Collections:Aurora harvest 7
Molecular and Biomedical Science publications

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