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Testing hypotheses about genes using genomic and proteomic data
  
By
Edward R. Winstead


Genes with similar expression patterns tend to produce proteins that interact. And proteins that interact tend to have related functions in cells. By linking information about gene expression and protein interactions for an organism, researchers can predict the biological roles of unknown genes. But data on genes and proteins are not usually integrated, and researchers have turned to bioinformatics to make sense of it all.


Detail of transcriptome-interactome correlation maps. View larger

Marc Vidal, of Harvard Medical School in Boston, and colleagues have created a statistical method for arranging gene expression data and protein interaction data in tables, or maps, to be analyzed in two or three dimensions. Testing the strategy in yeast, the researchers generated hypotheses about the functions of genes using different combinations of data. They found that analyzing the data on gene expression and protein interactions together, rather than separately, led to the most informative predictions.

The strategy of integrating genomic and proteomic data “can be used to improve the quality of hypotheses based on the information from both approaches,” the researchers write in Nature Genetics. The study, they add, provides “the first global evidence that genes with similar expression profiles are more likely to encode interacting proteins.”

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Ge, H. et al. Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nat Genet. Published online November 5, 2001.
 

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