Evolution and Selection in Yeast Promoters: Analyzing the Combined Effect of Diverse Transcription Factor Binding Sites

Daniela Raijman, TAU

In comparative genomics one analyzes jointly evolutionarily related species in order to identify conserved and diverged sequences and to infer their function. While such studies enabled the detection of conserved sequences in large genomes and discovered specific motifs that are surprisingly conserved in those regions, the evolutionary dynamics of regulatory regions as a whole remains poorly understood. In this talk we present a probabilistic, principled model for the evolution of promoter regions in yeast, combining the effects of regulatory interactions of many different transcription factors. The model expresses explicitly the selection forces acting on transcription factor binding sites, and its likelihood can be used for scoring the goodness of fit between a regulatory model and a set of promoter alignments. We develop algorithms to compute likelihood and to learn de-novo collections of transcription factor binding motifs and their selection parameters. Using the new techniques, we examine the evolutionary dynamics in Saccharomyces species promoters. Analyses of an evolutionary model constructed using all known transcription factor binding motifs and of a model learned from the data automatically reveal that surprisingly weak selection is affecting most binding sites. Moreover, according to our estimates, strong binding sites are constraining only a minor fraction of the yeast promoter sequence that is under selection. Our study demonstrates how detailed understanding of the complex evolutionary dynamics in non-coding regions emerges from proper formalization of the evolutionary consequences of known regulatory mechanisms.