Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/79245
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Inferring microRNA-mRNA causal regulatory relationships from expression data
Author: Le, T.
Liu, L.
Tsykin, A.
Goodall, G.
Liu, B.
Sun, B.
Li, J.
Citation: Bioinformatics, 2013; 29(6):765-771
Publisher: Oxford Univ Press
Issue Date: 2013
ISSN: 1367-4803
1460-2059
Statement of
Responsibility: 
Thuc Duy Le, Lin Liu, Anna Tsykin, Gregory J. Goodall, Bing Liu, Bing-Yu Sun and Jiuyong Li
Abstract: Motivation: microRNAs (miRNAs) are known to play an essential role in the post-transcriptional gene regulation in plants and animals. Currently, several computational approaches have been developed with a shared aim to elucidate miRNA–mRNA regulatory relationships. Although these existing computational methods discover the statistical relationships, such as correlations and associations between miRNAs and mRNAs at data level, such statistical relationships are not necessarily the real causal regulatory relationships that would ultimately provide useful insights into the causes of gene regulations. The standard method for determining causal relationships is randomized controlled perturbation experiments. In practice, however, such experiments are expensive and time consuming. Our motivation for this study is to discover the miRNA–mRNA causal regulatory relationships from observational data. Results: We present a causality discovery-based method to uncover the causal regulatory relationship between miRNAs and mRNAs, using expression profiles of miRNAs and mRNAs without taking into consideration the previous target information. We apply this method to the epithelial-to-mesenchymal transition (EMT) datasets and validate the computational discoveries by a controlled biological experiment for the miR-200 family. A significant portion of the regulatory relationships discovered in data is consistent with those identified by experiments. In addition, the top genes that are causally regulated by miRNAs are highly relevant to the biological conditions of the datasets. The results indicate that the causal discovery method effectively discovers miRNA regulatory relationships in data. Although computational predictions may not completely replace intervention experiments, the accurate and reliable discoveries in data are cost effective for the design of miRNA experiments and the understanding of miRNA–mRNA regulatory relationships. Availability: The R scripts are in the Supplementary material.
Keywords: Cell Line, Tumor; Animals; MicroRNAs; RNA, Messenger; Gene Expression Profiling; Gene Expression Regulation; Algorithms; Epithelial-Mesenchymal Transition
Rights: © The Author 2013. Published by Oxford University Press. All rights reserved.
RMID: 0020126472
DOI: 10.1093/bioinformatics/btt048
Appears in Collections:Molecular and Biomedical Science publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.