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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Local network topology in human protein interaction data predicts functional association
1Department of Bioinformatics & Computational Biology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, United States of America.
Plos One
|July 31, 2009
Summary
This study introduces a new algorithm to predict protein function from protein-protein interaction networks. The method improves functional association predictions in humans, identifying novel pathway players.
Area of Science:
- Systems Biology
- Bioinformatics
- Genomics
Background:
- High-throughput techniques generate vast protein-protein interaction (PPI) data, necessitating automated methods for functional inference.
- Local network topology, specifically shared neighbors between proteins, has previously predicted functional associations in yeast PPI networks.
Purpose of the Study:
- To develop and apply an improved algorithm for genome-wide functional inference in human PPI networks.
- To systematically measure and reduce the influence of hub proteins on predicting function-associated protein pairs.
Main Methods:
- Developed a novel algorithm to analyze human PPI networks and infer functional associations.
- Utilized Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations as benchmarks for evaluating functional relevance.
- Applied the algorithm to human PPI data, identifying significant functional associations and clustering proteins based on these associations.
Main Results:
- Identified 4,233 significant functional associations among 1,754 proteins in the human PPI network.
- Assigned 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with low false discovery rates (<21% for KEGG, <30% for GO).
- Discovered a subcluster highly enriched in the TGF-beta signaling pathway (P<10(-50)) and identified potential new players in six signaling pathways from other subclusters.
Conclusions:
- The study provides a reliable, common neighbor-based prediction method for large-scale functional annotation in the post-genomic era.
- The developed algorithm effectively reduces hub protein influence and enhances the accuracy of functional association predictions.
- Functional inferences from analyzed subclusters suggest new avenues for experimental investigation in various signaling pathways.
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These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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