Article

Zhu, J. et al. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nature Genet. 40, 854-861

Rosetta Inpharmatics, LLC, Seattle, Washington 98109, USA.
Nature Genetics (Impact Factor: 29.65). 08/2008; 40(7):854-61. DOI: 10.1038/ng.167
Source: PubMed

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.

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Available from: Roger Bumgarner, Feb 19, 2014
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    • "Whereas interaction networks can present a global and holistic view of the interacting elements directly or indirectly involved in disease progression, probabilistic causal networks can elucidate causal relationships as well as potential mechanisms (Zhu et al., 2008, 2012). Bayesian networks represent one class of probabilistic causal modeling approaches that are in widespread use today. "
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    • "Indeed, integrative genomics applications of BNs have become increasingly attractive [9]. In a comparison study between BNs inferred from only expression data and BNs inferred from expression together with other types of genomic data, the combination of multiple genomic data types results in increased performance [10]. This agrees with our own experience: in our previous work using earlier versions of the CGBayesNets software we identified eQTLs in cancer datasets [11] and predicted leukemia types by integrating single nucleotide polymorphisms (SNPs) and messenger ribonucleic acid (mRNA) expression levels [8]. "
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    • "Although, PPI data were used previously (Zhu et al., 2008) in the context of GRN inference, the approach used by previous researchers was very different from the approach used in this study. For instance, protein interactions among target genes were used by Zhu et al. (2008) to determine co-regulation of multiple genes. Here, we use protein interaction among TFs to determine combinatorial regulations by multiple TFs. "
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