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Local network topology in human protein interaction data predicts functional association.

Li H, Liang S - PLoS ONE (2009)

Bottom Line: The application of our algorithms to human PPI data yielded 4,233 significant functional associations among 1,754 proteins.Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations.Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotation in this post-genomic era.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics & Computational Biology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, United States of America.

ABSTRACT
The use of high-throughput techniques to generate large volumes of protein-protein interaction (PPI) data has increased the need for methods that systematically and automatically suggest functional relationships among proteins. In a yeast PPI network, previous work has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional association. In this study we improved the prediction scheme by developing a new algorithm and applied it on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting function-associated protein pairs. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as benchmarks to compare and evaluate the function relevance. The application of our algorithms to human PPI data yielded 4,233 significant functional associations among 1,754 proteins. Further functional comparisons between them allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made functional inferences from detailed analysis on one subcluster highly enriched in the TGF-beta signaling pathway (P<10(-50)). Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotation in this post-genomic era.

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Examples of subclusters derived from the significant 4,233 protein associations.Apparently each of them belongs to the same functional module in which they perform similar or the same biological functions.
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pone-0006410-g004: Examples of subclusters derived from the significant 4,233 protein associations.Apparently each of them belongs to the same functional module in which they perform similar or the same biological functions.

Mentions: In the cluster of 1,729 proteins, most of the functionally related proteins were correctly clustered into their corresponding functional modules, in which they are characterized by similar functions or the same pathway (Fig. 4). The largest subcluster derives directly from the root of the whole cluster and consists of 959 proteins; the second-largest subcluster has only 51 members (Fig. S2). We cut the 959-member subcluster with different cutoff values and analyzed the corresponding subclusters by using both manual inspection and Ingenuity Pathway Analysis (IPA). We conducted a detailed analysis for one prominent subcluster (the subcluster related to the TGF-β signaling pathway) as a reference.


Local network topology in human protein interaction data predicts functional association.

Li H, Liang S - PLoS ONE (2009)

Examples of subclusters derived from the significant 4,233 protein associations.Apparently each of them belongs to the same functional module in which they perform similar or the same biological functions.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2713831&req=5

pone-0006410-g004: Examples of subclusters derived from the significant 4,233 protein associations.Apparently each of them belongs to the same functional module in which they perform similar or the same biological functions.
Mentions: In the cluster of 1,729 proteins, most of the functionally related proteins were correctly clustered into their corresponding functional modules, in which they are characterized by similar functions or the same pathway (Fig. 4). The largest subcluster derives directly from the root of the whole cluster and consists of 959 proteins; the second-largest subcluster has only 51 members (Fig. S2). We cut the 959-member subcluster with different cutoff values and analyzed the corresponding subclusters by using both manual inspection and Ingenuity Pathway Analysis (IPA). We conducted a detailed analysis for one prominent subcluster (the subcluster related to the TGF-β signaling pathway) as a reference.

Bottom Line: The application of our algorithms to human PPI data yielded 4,233 significant functional associations among 1,754 proteins.Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations.Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotation in this post-genomic era.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics & Computational Biology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, United States of America.

ABSTRACT
The use of high-throughput techniques to generate large volumes of protein-protein interaction (PPI) data has increased the need for methods that systematically and automatically suggest functional relationships among proteins. In a yeast PPI network, previous work has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional association. In this study we improved the prediction scheme by developing a new algorithm and applied it on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting function-associated protein pairs. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as benchmarks to compare and evaluate the function relevance. The application of our algorithms to human PPI data yielded 4,233 significant functional associations among 1,754 proteins. Further functional comparisons between them allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made functional inferences from detailed analysis on one subcluster highly enriched in the TGF-beta signaling pathway (P<10(-50)). Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotation in this post-genomic era.

Show MeSH