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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Acknowledgements
Work at Princeton was supported by National Institute of Mental Health grant R37 MH059520 and a James S. McDonnell Foundation Centennial Fellowship to L.K., and Center grant P50GM071508 from the National Institute of General Medical Science to the Lewis-Sigler Institute. We thank J. Whittle for generating GPA1 allele swap data, S. Iyer for performing some of the deletion strain validation experiments and A. Deutschbauer (Lawrence Berkeley National Laboratory) for providing us with the MKT1 allele swap strain YAD350. We would also like to thank R. Ireton for her careful reading and editing of this manuscript.
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E.N.S., B.D., R.B.B. and L.K. constructed and characterized the genetically modified yeast strains. J.Z., B.Z. and E.E.S. carried out the coexpression and Bayesian network analyses and performed bioinformatic analyses. E.N.S., B.D., R.B.B., L.K. and R.E.B. aided in the data analysis. All authors were involved in the study design and interpretation of the experimental results, and discussed the results and commented on the manuscript. J.Z., B.Z. and E.E.S. designed the study, developed methods, analyzed the data and wrote the paper.
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J.Z., B.Z. and E.E.S. work for Merck and own stock in the company.
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Zhu, J., Zhang, B., Smith, E. et al. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat Genet 40, 854–861 (2008). https://doi.org/10.1038/ng.167
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DOI: https://doi.org/10.1038/ng.167
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