Deciphering Gene Regulatory Networks using DNA sequence

Cellular differentiation and tissue specification depend in part on the establishment of specific transcriptional programs of gene expression. These programs result from the interpretation of genomic regulatory information by sequence-specific transcription factors. Decoding this information in sequenced genomes is a key issue. First, we will show that the interaction between a transcription factor and its DNA binding sites can be accurately described by a Potts model inspired from spin glass physics. Its examination reveals that transcription factors induce nearest-neighbor correlations in the flanking nucleotides of their binding sites. We expect that the gain in predictability power compared to the simple, widespread Position Weight Matrix model will be useful for the correct identification of regulatory interactions in the genome. However, such models cannot always be built, simply due to the lack of data for certain transcription factors. To bypass the need of extensive binding data for the prediction of regulatory interactions, we will present Imogene, a Bayesian, phylogeny-based algorithm designed to computationally identify the regulators that control gene expression in a set of co-regulated genes, without any a priori knowledge of those regulators. Starting with known regulatory sequences in a reference species as a training set, the algorithm uses the over-representation and conservation of DNA sequences among related species to predict putative regulators de novo along with other regulatory regions in the genome with a similar regulatory output. We will present several biological applications of this algorithm both in Drosophila and vertebrates. Finally, we will give perspectives of this work for the interactome (network of regulatory interactions) and the diseasome (network of diseases) in view of the recently available data from the ENCODE project.