Characterizing the regulatory logic of transcriptional control at the DNA sequence level by ensembles of thermodynamic models

Understanding how the genome encodes the regulatory logics of transcription is a main challenge of the post-genomic era to be overcome with the aid of customized computational tools. We report an automatic framework for analyzing an ensemble of fittings to data of a thermodynamics-based sequence-level model for transcriptional regulation. The fittings are clustered accordingly with their intrinsic regulatory logics. A multiscale analysis enables visualization of quantitative features resulting from the deconvolution of the regulatory profile provided by multiple transcription factors interacting with the locus of a gene. Quantitative experimental data on reporters driven by the whole locus of the even-skipped gene in blastoderm of Drosophila embryos was used for validating our approach. A few clusters of highly active DNA binding sites within the enhancers collectively modulate even-skipped gene transcription. Analysis of variable enhancers’ length shows the importance of bound protein-protein interactions for transcriptional regulation.