Finding Informative Regulatory Elements

Noam Slomin

Department of Physics/Lewis Sigler Institute , Princeton University

Gene expression is regulated by protein transcription factors that bind at particular DNA or RNA sites in a sequence specific manner. A comprehensive characterization of these functional non-coding elements, or motifs, remains a formidable challenge, especially for higher eukaryotes. I will present a computational framework that utilizes a simple information-theoretic measure to predict functional motifs from experimental data. While existing methods are typically designed to analyze a particular data type, our approach is applicable to any type of experimental data, thus opening new fronts in which computational analysis can yield biologically meaningful predictions. Moreover, our analysis inherently highlights various biological aspects associated with the predicted motifs, including possible biophysical constraints over the motif's location and orientation, and potential cooperation between different motifs. To illustrate the generality of our approach we apply it to all major eukaryotes model organisms, including yeast, worm, fly, mouse, and human, as well as to the malaria parasite. Extensive build-in statistical tests are applied to minimize the rate of false-positive predictions. Our results often match known functional motifs, and further include highly significant novel predictions of potential biological interest. As a shorthand for our methodology we use the acronym FIRE, standing for Finding Informative Regulatory Elements.

Based on joint work with Olivier Elemento and Saeed Tavazoie.