MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model

BMC Bioinformatics

Software

David Arenillas¹  Elodie Portales-Casamar¹  Wyeth W Wasserman¹  Michèle Moes²  Evelyne Friederich²  Guillaume Vetter²  Antony Le Béchec²  Charles-Henri Lecellier³  Charles Theillet⁴  Anne Saumet⁴  Pierre-Joachim Zindy⁵ 

[1] Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, University of British Columbia, 950 West 28th Avenue, V5Z 4H4, Vancouver, BC, Canada;Cytoskeleton and Cell Plasticity lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511, Luxembourg, Luxembourg;Institut de Génétique Moléculaire de Montpellier UMR 5535 CNRS, 1919 route de Mende, F-34293, Montpellier cedex 5, France;Institut de Recherche en Cancérologie de Montpellier INSERM U896, Université Montpellier1, CRLC Val d'Aurelle Paul Lamarque, F-34298, Montpellier, France;Structure and Function of the Cell Nucleus, Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montréal, Québec, Canada;

关键词: Regulatory Motif;    miRNA Gene;    Interaction Graph;    miRNA Regulation;    JASPAR Database;   

DOI  :  10.1186/1471-2105-12-67

 received in 2010-07-27, accepted in 2011-03-04,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundTo understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date.ResultsTo identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs). Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL). In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT), an evolutionary-conserved process which is implicated in embryonic development and disease.ConclusionsMIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http://mironton.uni.lu which will be updated on a regular basis.

【 授权许可】

Unknown   
© Le Béchec et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.