Article abstract
Nature Genetics 40, 854 - 861 (2008)
Published online: 15 June 2008 | doi:10.1038/ng.167
Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks
Jun Zhu1, Bin Zhang1, Erin N Smith2,3, Becky Drees4, Rachel B Brem5, Leonid Kruglyak2, Roger E Bumgarner4 & Eric E Schadt1
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.
- Rosetta Inpharmatics, LLC, Seattle, Washington 98109, USA.
- Lewis-Sigler Institute for Integrative Genomics and Department of Ecology and Evolutionary Biology, Princeton University, Carl Icahn Laboratory, Princeton, New Jersey 08544, USA.
- Department of Molecular and Cellular Biology, Box 357275, University of Washington, Seattle, Washington 98195, USA.
- Department of Microbiology, Box 358070, University of Washington, Seattle, Washington 98195, USA.
- Department of Molecular and Cell Biology, 304A Stanley Hall #3220, University of California, Berkeley, Berkeley, California 94720, USA.
Correspondence to: Eric E Schadt1 e-mail: eric_schadt@merck.com
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