IntegratingGenomicDataYeastRegNW_ng08Schadt - ARTICLES 2008...

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Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks Jun Zhu 1 , Bin Zhang 1 , Erin N Smith 2,3 , Becky Drees 4 , Rachel B Brem 5 , Leonid Kruglyak 2 , Roger E Bumgarner 4 & Eric E Schadt 1 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. Large-scale genetic, transcriptomic, proteomic and metabolomic datasets have enabled researchers to decipher the biological function of individual genes, pathways, and, more generally, biological net- works that drive complex phenotypes. However, the progress toward uncovering the mechanisms by which these genes lead to complex phenotypes has progressed at a slower rate. More recently, significant progress has been made by integrating multiple sources of data sampled from human and experimental populations to reconstruct networks that are predictive of complex phenotypes. A number of studies in a variety of species have demonstrated that predictive networks can be built by leveraging naturally occurring DNA variation to determine how such variation influences gene expression and other molecular phenotypes. By examining the effects of naturally occurring DNA variation on gene expression in segregating populations, other phenotypes can be examined with respect to these same DNA variations and ordered relative to the genes to infer causality 1–4 . Network reconstructions based on protein–protein interaction data 5 , metabolomic data 6 and literature data 7 are also now becoming more routine. The common theme among these reconstruction efforts is the organization of vast amounts of molecular data into networks that capture fundamental properties of complex systems in states that give rise to complex phenotypes. Although advances in the application of network reconstruction algorithms to high-dimensional biological data are being applied to a number of distinct data types, such as protein–protein interaction data 5 , metabolomic data 6 and published gene-gene relationship data 7 ,
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This note was uploaded on 04/06/2010 for the course COMPUTER S COSC1520 taught by Professor Paul during the Spring '09 term at York University.

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IntegratingGenomicDataYeastRegNW_ng08Schadt - ARTICLES 2008...

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