Computational discovery of regulatory elements in a continuous expression space

Mathieu Lajoie 1 Olivier Gascuel 1 Vincent Lefort 1 Laurent Brehelin 1, *
* Corresponding author
1 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Approaches for regulatory element discovery from gene expression data usually rely on clustering algorithms to partition the data into clusters of co-expressed genes. Gene regulatory sequences are then mined to find over-represented motifs in each cluster. However, this ad-hoc partition rarely fits the biological reality. We propose a novel method called RED2 that avoids data clustering by estimating motif densities locally around each gene. We show that RED2 detects numerous motifs not detected by clustering-based approaches, and that most of these correspond to characterized motifs. RED2 can be accessed online with a user-friendly interface at http://www.atgc-montpellier.fr/RED2/.
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Mathieu Lajoie, Olivier Gascuel, Vincent Lefort, Laurent Brehelin. Computational discovery of regulatory elements in a continuous expression space. Genome Biology, BioMed Central, 2012, 13 (11), pp.41. ⟨lirmm-00796453⟩

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