Computational discovery of regulatory elements in a continuous expression space

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 RED(2 )that avoids data clustering by estimating motif densities locally around each gene. We show that RED(2 )detects numerous motifs not detected by clustering-based approaches, and that most of these correspond to characterized motifs. RED(2 )can be accessed online through a user-friendly interface.