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

Antony Le Béchec1, Elodie Portales-Casamar2, Guillaume Vetter1, Michèle Moes1, Pierre-Joachim Zindy3, Anne Saumet4, David Arenillas2, Charles Theillet4, Wyeth W Wasserman2, Charles-Henri Lecellier5 and Evelyne Friederich1*

Author Affiliations

1 Cytoskeleton and Cell Plasticity lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg

2 Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, University of British Columbia, 950 West 28th Avenue, Vancouver, BC V5Z 4H4, Canada

3 Structure and Function of the Cell Nucleus, Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montréal (Québec), Canada

4 Institut de Recherche en Cancérologie de Montpellier INSERM U896, Université Montpellier1, CRLC Val d'Aurelle Paul Lamarque, Montpellier, F-34298, France

5 Institut de Génétique Moléculaire de Montpellier UMR 5535 CNRS, 1919 route de Mende, F-34293 Montpellier cedex 5, France

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BMC Bioinformatics 2011, 12:67  doi:10.1186/1471-2105-12-67

Published: 4 March 2011

Additional files

Additional file 1:

MIR@NT@N predictions for TF→miRNA regulations in Qiu et al. Table providing MIR@NT@N database predictions (maximum score, maximum length and number of TFBS) for TF→miRNA regulations described in Qiu et al., for the 19 human TFs found in common.

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