Tissue-specific subnetworks and characteristics of publicly available human protein interaction databases
Tissue-specific subnetworks and characteristics of publicly available human protein interaction databases
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DOI：
10.1093/bioinformatics/btr414
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发表时间：
2011-09-01
期刊：
影响因子：
5.8
通讯作者：
Kitano, Hiroaki
中科院分区：
文献类型：
--
作者：
Lopes, Tiago J. S.;Schaefer, Martin;Kitano, Hiroaki
动机：蛋白质-蛋白质相互作用（PPI）数据库是广泛用于研究细胞通路和网络的工具；然而，有几个可用的数据库仍然不能解释细胞类型特异性差异。在这里，我们评估了六个相互作用数据库的特征，纳入了组织特异性基因表达信息，最后调查了科学文献中最受欢迎的蛋白质是否参与了高质量的相互作用。结果：我们发现被评估的数据库在节点连通性方面具有可比性（即相互作用伙伴较少的蛋白质在其他数据库中也具有很少的相互作用伙伴），但在相互作用伙伴的身份方面可能存在差异。我们还观察到，组织特异性表达信息的结合显著地改变了相互作用的格局，最后，我们证明了许多最深入研究的蛋白质都参与了与低置信度分数相关的相互作用。总之，相互作用数据库是有价值的研究工具，但可能导致对相互作用或途径的不同预测。通过结合器官和细胞类型特异性基因表达的数据集，以及通过获得最“流行”的蛋白质的额外相互作用证据，可以提高预测的准确性。
Motivation: Protein-protein interaction (PPI) databases are widely used tools to study cellular pathways and networks; however, there are several databases available that still do not account for cell type-specific differences. Here, we evaluated the characteristics of six interaction databases, incorporated tissue-specific gene expression information and finally, investigated if the most popular proteins of scientific literature are involved in good quality interactions.Results: We found that the evaluated databases are comparable in terms of node connectivity (i.e. proteins with few interaction partners also have few interaction partners in other databases), but may differ in the identity of interaction partners. We also observed that the incorporation of tissue-specific expression information significantly altered the interaction landscape and finally, we demonstrated that many of the most intensively studied proteins are engaged in interactions associated with low confidence scores. In summary, interaction databases are valuable research tools but may lead to different predictions on interactions or pathways. The accuracy of predictions can be improved by incorporating datasets on organ-and cell type-specific gene expression, and by obtaining additional interaction evidence for the most 'popular' proteins.