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Nat Genet. 2008 Jul;40(7):854-61. doi: 10.1038/ng.167. Epub 2008 Jun 15.

Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks.

Author information

1
Rosetta Inpharmatics, LLC, Seattle, Washington 98109, USA.

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.

PMID:
18552845
PMCID:
PMC2573859
DOI:
10.1038/ng.167
[Indexed for MEDLINE]
Free PMC Article

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