Journal Article

Identification of microRNA regulatory modules in <i>Arabidopsis</i> via a probabilistic graphical model

Je-Gun Joung and Zhangjun Fei

in Bioinformatics

Volume 25, issue 3, pages 387-393
Published in print February 2009 | ISSN: 1367-4803
Published online December 2008 | e-ISSN: 1460-2059 | DOI:
Identification of microRNA regulatory modules in Arabidopsis via a probabilistic graphical model

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Motivation: MicroRNAs miRNAs play important roles in gene regulation and are regarded as key components in gene regulatory pathways. Systematically understanding functional roles of miRNAs is essential to define core transcriptional units regulating key biological processes. Here, we propose a method based on the probabilistic graphical model to identify the regulatory modules of miRNAs and the core regulatory motifs involved in their ability to regulate gene expression.

Results: We applied our method to datasets of different sources from Arabidopsis consisting of miRNA-target pair information, upstream sequences of miRNAs, transcriptional regulatory motifs and gene expression profiles. The graphical model used in this study can efficiently capture the relationship between miRNAs and diverse conditions such as various developmental processes, thus allowing us to detect functionally correlated miRNA regulatory modules involved in specific biological processes. Furthermore, this approach can reveal core transcriptional elements associated with their miRNAs. The proposed method found clusters of miRNAs, as well as putative regulators controlling the expression of miRNAs, which were highly related to diverse developmental processes of Arabidopsis. Consequently, our method can provide hypothetical miRNA regulatory circuits for functional testing that represent transcriptional events of miRNAs and transcriptional factors involved in gene regulatory pathways.


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5034 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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