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Protein Complex-Based Analysis Framework for High-Throughput Data Sets
DOI:10.1126/scisignal.2003629.png)
摘要
En 中文
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.
Keyword:
MOLECULAR INTERACTION DATABASE
RECEPTOR TYROSINE KINASE
INTERACTION NETWORK
INSULIN-RECEPTOR
CELL-SIZE
PHOSPHORYLATION
GROWTH
INTEGRATION
EXPRESSION
LANDSCAPE
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期刊
IF:
6.6
论文数:
3.0K
被引数:
1.4W
机构
引用论文
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A human phenome-interactome network of protein complexes implicated in genetic disorders
NATURE BIOTECHNOLOGY
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