MOTIVATION: With the growing availability of high-throughput protein-protein interaction (PPI) data, it has become possible to consider how a protein's local (or global) network characteristics predict its function. RESULTS: We introduce a graph-theoretic approach that identifies key regulatory proteins in an organism by analyzing proteins' local PPI network structure. We apply the method to the yeast genome and describe several properties of the resulting set of regulatory hubs. Finally, we demonstrate how the identified hubs and putative target gene sets can be used to identify causative, functional regulators of differential gene expression linked to human disease. AVAILABILITY: Code is available at http://bcb.cs.tufts.edu/hubcomps. CONTACT: firstname.lastname@example.org.
|Evidence ID||Analyze ID||Interactor||Interactor Systematic Name||Interactor||Interactor Systematic Name||Type||Assay||Annotation||Action||Modification||Phenotype||Source||Reference||Note|
|Evidence ID||Analyze ID||Gene||Gene Systematic Name||Gene Ontology Term||Gene Ontology Term ID||Qualifier||Aspect||Method||Evidence||Source||Assigned On||Reference||Annotation Extension|
|Evidence ID||Analyze ID||Gene||Gene Systematic Name||Phenotype||Experiment Type||Experiment Type Category||Mutant Information||Strain Background||Chemical||Details||Reference|
|Evidence ID||Analyze ID||Regulator||Regulator Systematic Name||Target||Target Systematic Name||Experiment||Conditions||Strain||Source||Reference|