Prediction of yeast protein-protein interaction network: insights from the Gene Ontology and annotations Export

@article{Wu2006, abstract = {A map of protein-protein interactions provides valuable insight into the cellular function and machinery of a proteome. By measuring the similarity between two Gene Ontology (GO) terms with a relative specificity semantic relation, here, we proposed a new method of reconstructing a yeast protein-protein interaction map that is solely based on the GO annotations. The method was validated using high-quality interaction datasets for its effectiveness. Based on a Z-score analysis, a positive dataset and a negative dataset for protein-protein interactions were derived. Moreover, a gold standard positive (GSP) dataset with the highest level of confidence that covered 78\% of the high-quality interaction dataset and a gold standard negative (GSN) dataset with the lowest level of confidence were derived. In addition, we assessed four high-throughput experimental interaction datasets using the positives and the negatives as well as GSPs and GSNs. Our predicted network reconstructed from GSPs consists of 40 753 interactions among 2259 proteins, and forms 16 connected components. We mapped all of the MIPS complexes except for homodimers onto the predicted network. As a result, [\~{}]35\% of complexes were identified interconnected. For seven complexes, we also identified some nonmember proteins that may be functionally related to the complexes concerned. This analysis is expected to provide a new approach for predicting the protein-protein interaction maps from other completely sequenced genomes with high-quality GO-based annotations. 10.1093/nar/gkl219}, author = {Wu, Xiaomei and Zhu, Lei and Guo, Jie and Zhang, Da-Yong and Lin, Kui}, citeulike-article-id = {623787}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/nar/gkl219}, citeulike-linkout-1 = {http://nar.oxfordjournals.org/cgi/content/abstract/34/7/2137}, citeulike-linkout-2 = {http://www.ingentaconnect.com/content/oup/nar/2006/00000034/00000007/art02137}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/16641319}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=16641319}, day = {26}, doi = {10.1093/nar/gkl219}, issn = {0305-1048}, journal = {Nucl. Acids Res.}, keywords = {gene-ontology, semantic-similarity}, month = {April}, number = {7}, pages = {2137--2150}, posted-at = {2009-09-22 04:51:07}, priority = {2}, publisher = {Oxford University Press}, title = {Prediction of yeast protein-protein interaction network: insights from the Gene Ontology and annotations}, url = {http://dx.doi.org/10.1093/nar/gkl219}, volume = {34}, year = {2006} }

TY - JOUR ID - Wu2006 L3 - citeulike-article-id:623787 N2 - A map of protein-protein interactions provides valuable insight into the cellular function and machinery of a proteome. By measuring the similarity between two Gene Ontology (GO) terms with a relative specificity semantic relation, here, we proposed a new method of reconstructing a yeast protein-protein interaction map that is solely based on the GO annotations. The method was validated using high-quality interaction datasets for its effectiveness. Based on a Z-score analysis, a positive dataset and a negative dataset for protein-protein interactions were derived. Moreover, a gold standard positive (GSP) dataset with the highest level of confidence that covered 78% of the high-quality interaction dataset and a gold standard negative (GSN) dataset with the lowest level of confidence were derived. In addition, we assessed four high-throughput experimental interaction datasets using the positives and the negatives as well as GSPs and GSNs. Our predicted network reconstructed from GSPs consists of 40 753 interactions among 2259 proteins, and forms 16 connected components. We mapped all of the MIPS complexes except for homodimers onto the predicted network. As a result, [~]35% of complexes were identified interconnected. For seven complexes, we also identified some nonmember proteins that may be functionally related to the complexes concerned. This analysis is expected to provide a new approach for predicting the protein-protein interaction maps from other completely sequenced genomes with high-quality GO-based annotations. 10.1093/nar/gkl219 IS - 7 JF - Nucl. Acids Res. SN - 0305-1048 EP - 2150 TI - Prediction of yeast protein-protein interaction network: insights from the Gene Ontology and annotations PB - Oxford University Press VL - 34 SP - 2137 KW - gene-ontology KW - semantic-similarity AU - Wu, Xiaomei AU - Zhu, Lei AU - Guo, Jie AU - Zhang, Da-Yong AU - Lin, Kui PY - 2006/04/26/ UR - http://dx.doi.org/10.1093/nar/gkl219 ER -