Authors: Zhu J; Zhang B; Smith EN; Drees B; Brem RB; Kruglyak L; Bumgarner RE; Schadt EE
Abstract: A key goal of biology is to construct networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments, in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. We show that a network reconstructed by integrating genotypic, TFBS and PPI data is the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. We also show that the network can elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. We then prospectively validate predictions, providing direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types.
Keywords: Bayes Theorem; Cluster Analysis; Crosses, Genetic; *Gene Expression Regulation, Fungal; Gene Regulatory Networks/*physiology; *Genome, Fungal; Genomics/methods; Models, Statistical; Organisms, Genetically Modified; Quantitative Trait Loci; Saccharomyces cerevisiae/*genetics; Transcription Factors/genetics/physiology|
Journal: Nature genetics Volume: 40 Issue: 7 Pages: 854-61 Date: June 17, 2008 PMID: 18552845 |
Network
Zhu J, Zhang B, Smith EN, Drees B, Brem RB, Kruglyak L, Bumgarner RE, Schadt EE (2008) Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nature genetics 40: 854-61.
Comment on This Data Unit