Skip Navigation


Bioinformatics Advance Access originally published online on December 4, 2008
Bioinformatics 2009 25(3):387-393; doi:10.1093/bioinformatics/btn626
This Article
Right arrow Full Text
Right arrow Full Text (Print PDF)
Right arrow Supplementary Data
Right arrow All Versions of this Article:
25/3/387    most recent
btn626v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Joung, J.-G.
Right arrow Articles by Fei, Z.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Joung, J.-G.
Right arrow Articles by Fei, Z.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Identification of microRNA regulatory modules in Arabidopsis via a probabilistic graphical model

Je-Gun Joung 1 and Zhangjun Fei 1,2,*

1Boyce Thompson Institute for Plant Research, Cornell University and 2USDA Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA

*To whom correspondence should be addressed.


   Abstract

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.

Contact: zf25{at}cornell.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: John Quackenbush


Received on August 26, 2008; revised on November 11, 2008; accepted on December 1, 2008

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.