title: Inferring regulatory mechanisms from patterns of evolutionary divergence.
abstract: The number of sequenced species is increasing at a staggering rate, calling for new approaches for incorporating evolutionary information in the study of biological mechanisms. Evolutionary conservation is widely used for assigning a function to new proteins and for predicting functional coding or non-coding sequences. Here, we argue for a complementary approach that focuses on the divergence of regulatory programs. Regulatory mechanisms can be learned from patterns of evolutionary divergence in regulatory properties such as gene expression, transcription factor binding or nucleosome positioning. We review examples of this concept using yeast as a model system, and highlight a hybrid-based approach that is highly instrumental in this analysis.
authors.affiliation: Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel.
authors.forename: Itay
authors.initials: I
authors.lastname: Tirosh
authors.forename: Naama
authors.initials: N
authors.lastname: Barkai
mesh_terms: D000818:Animals; D002678:Chimera / Q000235:genetics* / Q000378:metabolism; D017124:Conserved Sequence / Q000235:genetics; D019143:Evolution, Molecular; D015966:Gene Expression Regulation, Fungal; D018740:Genetic Heterogeneity; D016681:Genome, Fungal; D023281:Genomics; D051379:Mice; D009154:Mutation; D009707:Nucleosomes / Q000235:genetics / Q000378:metabolism; D011401:Promoter Regions, Genetic; D020871:RNA Stability; D012333:RNA, Messenger / Q000096:biosynthesis / Q000235:genetics; D012440:Saccharomyces / Q000235:genetics* / Q000378:metabolism; D012641:Selection, Genetic; D013045:Species Specificity; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D009707:Nucleosomes; D012333:RNA, Messenger; D014157:Transcription Factors