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Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

Stefanie De Bodt (UGent) , Sebastian Proost (UGent) , Klaas Vandepoele (UGent) , Pierre Rouzé (UGent) and Yves Van de Peer (UGent)
(2009) BMC Genomics. 10(288). p.1-15
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Abstract
Background: Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome. Results: In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization) and components (e.g. ARPs, actin-related proteins) exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively. Conclusion: We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.
Keywords
PROBE LEVEL DATA, MOLECULAR INTERACTION DATABASE, SACCHAROMYCES-CEREVISIAE, INTERACTION NETWORKS, INTERACTION MAP, PROBABILISTIC MODEL, EXPRESSION DATA, TARGET GENES, C-ELEGANS, SCALE DATA

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Chicago
De Bodt, Stefanie, Sebastian Proost, Klaas Vandepoele, Pierre Rouzé, and Yves Van de Peer. 2009. “Predicting Protein-protein Interactions in Arabidopsis Thaliana Through Integration of Orthology, Gene Ontology and Co-expression.” BMC Genomics 10 (288): 1–15.
APA
De Bodt, Stefanie, Proost, S., Vandepoele, K., Rouzé, P., & Van de Peer, Y. (2009). Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression. BMC Genomics, 10(288), 1–15.
Vancouver
1.
De Bodt S, Proost S, Vandepoele K, Rouzé P, Van de Peer Y. Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression. BMC Genomics. 2009;10(288):1–15.
MLA
De Bodt, Stefanie, Sebastian Proost, Klaas Vandepoele, et al. “Predicting Protein-protein Interactions in Arabidopsis Thaliana Through Integration of Orthology, Gene Ontology and Co-expression.” BMC Genomics 10.288 (2009): 1–15. Print.
@article{748950,
  abstract     = {Background: Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome.

Results: In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization) and components (e.g. ARPs, actin-related proteins) exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively.

Conclusion: We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.},
  author       = {De Bodt, Stefanie and Proost, Sebastian and Vandepoele, Klaas and Rouz{\'e}, Pierre and Van de Peer, Yves},
  issn         = {1471-2164},
  journal      = {BMC Genomics},
  language     = {eng},
  number       = {288},
  pages        = {1--15},
  title        = {Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression},
  url          = {http://dx.doi.org/10.1186/1471-2164-10-288},
  volume       = {10},
  year         = {2009},
}

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