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

@article{citeulike:2904355, 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.}, address = {Rosetta Inpharmatics, LLC, Seattle, Washington 98109, USA.}, author = {Zhu, Jun and Zhang, Bin and Smith, Erin N N. and Drees, Becky and Brem, Rachel B B. and Kruglyak, Leonid and Bumgarner, Roger E E. and Schadt, Eric E E. }, citeulike-article-id = {2904355}, doi = {10.1038/ng.167}, issn = {1546-1718}, journal = {Nature genetics}, keywords = {causality, network\_inference, nw\_modularity}, month = {June}, posted-at = {2008-06-26 18:39:04}, priority = {2}, title = {Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks.}, url = {http://dx.doi.org/10.1038/ng.167}, year = {2008} }

TY - JOUR ID - citeulike:2904355 L3 - citeulike-article-id:2904355 TI - Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. JF - Nature genetics AD - Rosetta Inpharmatics, LLC, Seattle, Washington 98109, USA. SN - 1546-1718 N2 - 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. KW - causality KW - network_inference KW - nw_modularity AU - Zhu, Jun AU - Zhang, Bin AU - Smith, Erin N AU - Drees, Becky AU - Brem, Rachel B AU - Kruglyak, Leonid AU - Bumgarner, Roger E AU - Schadt, Eric E PY - 2008/06/15/ UR - http://dx.doi.org/10.1038/ng.167 ER -