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Computational discovery of regulatory elements in a continuous expression space

Mathieu Lajoie*, Olivier Gascuel, Vincent Lefort and Laurent Bréhélin

Author affiliations

Méthodes et algorithmes pour la Bioinformatique, LIRMM, Univ. Montpellier 2, CNRS; 161 rue Ada, 34095 MONTPELLIER, France

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Citation and License

Genome Biology 2012, 13:R109  doi:10.1186/gb-2012-13-11-r109

Published: 27 November 2012


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 overrepresented 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 through a user-friendly interface.

motif discovery; gene regulation; co-expressed genes; clustering; k-nearest neighbors