Dataset Information

Using protein complexes to predict phenotypic effects of gene mutation.

ABSTRACT:

Background

Predicting the phenotypic effects of mutations is a central goal of genetics research; it has important applications in elucidating how genotype determines phenotype and in identifying human disease genes.

Results

Using a wide range of functional genomic data from the yeast Saccharomyces cerevisiae, we show that the best predictor of a protein's knockout phenotype is the knockout phenotype of other proteins that are present in a protein complex with it. Even the addition of multiple datasets does not improve upon the predictions made from protein complex membership. Similarly, we find that a proxy for protein complexes is a powerful predictor of disease phenotypes in humans.

Conclusion

We propose that identifying human protein complexes containing known disease genes will be an efficient method for large-scale disease gene discovery, and that yeast may prove to be an informative model system for investigating, and even predicting, the genetic basis of both Mendelian and complex disease phenotypes.

SUBMITTER: Fraser HB

PROVIDER: S-EPMC2258176 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Publications

Using protein complexes to predict phenotypic effects of gene mutation.

Fraser Hunter B HB Plotkin Joshua B JB

Genome biology 20070101 11

<h4>Background</h4>Predicting the phenotypic effects of mutations is a central goal of genetics research; it has important applications in elucidating how genotype determines phenotype and in identifying human disease genes.<h4>Results</h4>Using a wide range of functional genomic data from the yeast Saccharomyces cerevisiae, we show that the best predictor of a protein's knockout phenotype is the knockout phenotype of other proteins that are present in a protein complex with it. Even the additio ...[more]

PMID: 18042286

Similar Datasets

Project description:BackgroundMany of the functional units in cells are multi-protein complexes such as RNA polymerase, the ribosome, and the proteasome. For such units to work together, one might expect a high level of regulation to enable co-appearance or repression of sets of complexes at the required time. However, this type of coordinated regulation between whole complexes is difficult to detect by existing methods for analyzing mRNA co-expression. We propose a new methodology that is able to detect such higher order relationships.ResultsWe detect coordinated regulation of multiple protein complexes using logic analysis of gene expression data. Specifically, we identify gene triplets composed of genes whose expression profiles are found to be related by various types of logic functions. In order to focus on complexes, we associate the members of a gene triplet with the distinct protein complexes to which they belong. In this way, we identify complexes related by specific kinds of regulatory relationships. For example, we may find that the transcription of complex C is increased only if the transcription of both complex A AND complex B is repressed. We identify hundreds of examples of coordinated regulation among complexes under various stress conditions. Many of these examples involve the ribosome. Some of our examples have been previously identified in the literature, while others are novel. One notable example is the relationship between the transcription of the ribosome, RNA polymerase and mannosyltransferase II, which is involved in N-linked glycan processing in the Golgi.ConclusionsThe analysis proposed here focuses on relationships among triplets of genes that are not evident when genes are examined in a pairwise fashion as in typical clustering methods. By grouping gene triplets, we are able to decipher coordinated regulation among sets of three complexes. Moreover, using all triplets that involve coordinated regulation with the ribosome, we derive a large network involving this essential cellular complex. In this network we find that all multi-protein complexes that belong to the same functional class are regulated in the same direction as a group (either induced or repressed).

Dataset Information

Using protein complexes to predict phenotypic effects of gene mutation.

Background

Results

Conclusion

Publications

Using protein complexes to predict phenotypic effects of gene mutation.

Similar Datasets

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