Computational discovery of gene modules and regulatory networks

• Published in: Nature biotechnology 2003-11, Vol.21 (11), p.1337-1342
• Main Authors: Gifford, David K, Bar-Joseph, Ziv, Gerber, Georg K, Lee, Tong Ihn, Rinaldi, Nicola J, Yoo, Jane Y, Robert, François, Gordon, D Benjamin, Fraenkel, Ernest, Jaakkola, Tommi S, Young, Richard A
• Format: Article
• Language: English
• Subjects: Agriculture; Algorithms; Bioinformatics; Biological and medical sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Computer Simulation; Deoxyribonucleic acid; Diverse techniques; DNA; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; Gene Expression Regulation, Fungal - physiology; Genome, Fungal; letter; Life Sciences; Models, Genetic; Molecular and cellular biology; Regulatory Sequences, Nucleic Acid - genetics; Saccharomyces cerevisiae; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - metabolism; Saccharomyces cerevisiae Proteins - genetics; Saccharomyces cerevisiae Proteins - metabolism; Transcription Factors - genetics; Transcription Factors - metabolism; Transcription, Genetic - physiology; Yeasts
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/nbt890

We describe an algorithm for discovering regulatory networks of gene modules, GRAM (Genetic Regulatory Modules), that combines information from genome-wide location and expression data sets. A gene module is defined as a set of coexpressed genes to which the same set of transcription factors binds. Unlike previous approaches that relied primarily on functional information from expression data, the GRAM algorithm explicitly links genes to the factors that regulate them by incorporating DNA binding data, which provide direct physical evidence of regulatory interactions. We use the GRAM algorithm to describe a genome-wide regulatory network in Saccharomyces cerevisiae using binding information for 106 transcription factors profiled in rich medium conditions data* from over 500 expression experiments. We also present a genome-wide location analysis data set for regulators in yeast cells treated with rapamycin, and use the GRAM algorithm to provide biological insights into this regulatory network.*Note: In the version of this article initially published online, the word "and" was omitted from the fourth sentence of the abstract, altering the meaning. The sentence should read: "We use the GRAM algorithm to describe a genome-wide regulatory network in Saccharomyces cerevisiae using binding information for 106 transcription factors profiled in rich medium conditions and data from over 500 expression experiments." This mistake has been corrected for the HTML and print versions of the article.