%0 Journal Article %J Sci Signal %D 2013 %T Protein complex-based analysis framework for high-throughput data sets. %A Vinayagam, Arunachalam %A Hu, Yanhui %A Kulkarni, Meghana %A Roesel, Charles %A Sopko, Richelle %A Mohr, Stephanie E %A Perrimon, Norbert %K Animals %K Cell Cycle Proteins %K Data Mining %K Databases, Genetic %K drosophila melanogaster %K Drosophila Proteins %K Gene Expression Profiling %K High Mobility Group Proteins %K High-Throughput Screening Assays %K Humans %K Insulin %K Internet %K Molecular Sequence Annotation %K Multiprotein Complexes %K Protein Interaction Maps %K proteomics %K RNA Interference %K Saccharomyces cerevisiae %K Software %K Species Specificity %K Systems Biology %K Trans-Activators %X

Analysis of high-throughput data increasingly relies on pathway annotation and functional information derived from Gene Ontology. This approach has limitations, in particular for the analysis of network dynamics over time or under different experimental conditions, in which modules within a network rather than complete pathways might respond and change. We report an analysis framework based on protein complexes, which are at the core of network reorganization. We generated a protein complex resource for human, Drosophila, and yeast from the literature and databases of protein-protein interaction networks, with each species having thousands of complexes. We developed COMPLEAT (http://www.flyrnai.org/compleat), a tool for data mining and visualization for complex-based analysis of high-throughput data sets, as well as analysis and integration of heterogeneous proteomics and gene expression data sets. With COMPLEAT, we identified dynamically regulated protein complexes among genome-wide RNA interference data sets that used the abundance of phosphorylated extracellular signal-regulated kinase in cells stimulated with either insulin or epidermal growth factor as the output. The analysis predicted that the Brahma complex participated in the insulin response.

%B Sci Signal %V 6 %P rs5 %8 2013 Feb 26 %G eng %N 264 %1 http://www.ncbi.nlm.nih.gov/pubmed/23443684?dopt=Abstract %R 10.1126/scisignal.2003629