We present the GRAM (Genetic RegulAtory Modules) algorithm that identifies modules, collections of genes that share common regulators as well as expression profiles. The algorithm combines direct information from DNA binding experiments with indirect functional information from expression experiments. We use the algorithm to derive a genome-wide regulatory network using binding information for 106 transcription factors in Saccharomyces cerevisiae in rich media conditions. These results are validated using a number of high throughput and other methods. Additionally, we present several new datasets of genome-wide location analysis for regulators in cells treated with rapamycin, and use the GRAM algorithm to provide biological insights in this regulatory network.
