Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks.
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.
Zhu
Jun
J
Rosetta Inpharmatics, LLC, Seattle, Washington 98109, USA.
Zhang
Bin
B
Smith
Erin N
EN
Drees
Becky
B
Brem
Rachel B
RB
Kruglyak
Leonid
L
Bumgarner
Roger E
RE
Schadt
Eric E
EE
D001499:Bayes Theorem; D016000:Cluster Analysis; D003433:Crosses, Genetic; D015966:Gene Expression Regulation, Fungal; D053263:Gene Regulatory Networks / Q000502:physiology*; D016681:Genome, Fungal; D023281:Genomics / Q000379:methods; D015233:Models, Statistical; D030781:Organisms, Genetically Modified; D040641:Quantitative Trait Loci; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D014157:Transcription Factors / Q000235:genetics / Q000502:physiology
D014157:Transcription Factors