Abstract:
:Genomic and proteomic approaches can provide hypotheses concerning function for the large number of genes predicted from genome sequences. 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 projects, 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.
journal_name
Nat Genetjournal_title
Nature geneticsauthors
Ge H,Liu Z,Church GM,Vidal Mdoi
10.1038/ng776keywords:
subject
Has Abstractpub_date
2001-12-01 00:00:00pages
482-6issue
4eissn
1061-4036issn
1546-1718pii
ng776journal_volume
29pub_type
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