title: Computational discovery of regulatory elements in a continuous expression space.
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 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.
authors.forename: Mathieu
authors.initials: M
authors.lastname: Lajoie
authors.forename: Olivier
authors.initials: O
authors.lastname: Gascuel
authors.forename: Vincent
authors.initials: V
authors.lastname: Lefort
authors.forename: Laurent
authors.initials: L
authors.lastname: Bréhélin
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D015870:Gene Expression; D050436:Regulatory Elements, Transcriptional