Genomic and proteomic approaches can provide hypotheses concerning function for the large number of genes predicted from genome sequences1,2,3,4,5. Because of the artificial nature of the assays, however, the information from these high-throughput approaches should be considered with caution. Although it is possible that more meaningful hypotheses could be formulated by integrating the data from various functional genomic and proteomic projects6, it has yet to be seen to what extent the data can be correlated and how such integration can be achieved. We developed a 'transcriptome–interactome correlation mapping' strategy to compare the interactions between proteins encoded by genes that belong to common expression-profiling clusters with those between proteins encoded by genes that belong to different clusters. Using this strategy with currently available data sets for Saccharomyces cerevisiae, we provide the first global evidence that genes with similar expression profiles are more likely to encode interacting proteins. We show how this correlation between transcriptome and interactome data can be used to improve the quality of hypotheses based on the information from both approaches. The strategy described here may help to integrate other functional genomic and proteomic data, both in yeast and in higher organisms.
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We thank P. Lansburry for his support, Mr. and Ms. Fu for their financial help, R.E. Esposito for exchange of information, V. Rebel and members of the Vidal lab for insightful suggestions and M. Walhout, T. Brüls and N. Thierry-Mieg for reading the manuscript. This work was supported by grant 1 RO1 HG01715-01 from the National Human Genome Research Institute, awarded to M.V.
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Ge, H., Liu, Z., Church, G. et al. Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nat Genet 29, 482–486 (2001). https://doi.org/10.1038/ng776
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