title: Identifying regulatory networks by combinatorial analysis of promoter elements.
abstract: Several computational methods based on microarray data are currently used to study genome-wide transcriptional regulation. Few studies, however, address the combinatorial nature of transcription, a well-established phenomenon in eukaryotes. Here we describe a new approach using microarray data to uncover novel functional motif combinations in the promoters of Saccharomyces cerevisiae. In addition to identifying novel motif combinations that affect expression patterns during the cell cycle, sporulation and various stress responses, we observed regulatory cross-talk among several of these processes. We have also generated motif-association maps that provide a global view of transcription networks. The maps are highly connected, suggesting that a small number of transcription factors are responsible for a complex set of expression patterns in diverse conditions. This approach may be useful for modeling transcriptional regulatory networks in more complex eukaryotes.
authors.affiliation: Department of Genetics and Lipper Center for Computational Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Pilpel
authors.forename: P
authors.initials: P
authors.lastname: Sudarsanam
authors.forename: G M
authors.initials: GM
authors.lastname: Church
mesh_terms: D002453:Cell Cycle; D019295:Computational Biology; D004271:DNA, Fungal / Q000235:genetics; D020869:Gene Expression Profiling; D020411:Oligonucleotide Array Sequence Analysis; D011401:Promoter Regions, Genetic; D012045:Regulatory Sequences, Nucleic Acid; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics / Q000502:physiology; D013172:Spores, Fungal; D014158:Transcription, Genetic
chemical_list: D004271:DNA, Fungal