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.
CITATION STYLE
Lajoie, M., Gascuel, O., Lefort, V., & Bréhélin, L. (2012). Computational discovery of regulatory elements in a continuous expression space. Genome Biology, 13(11), R109. https://doi.org/10.1186/gb-2012-13-11-r109
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