JST ERATO KAWAOKA Infection-induced Host Responses Project, Tokyo, Japan.
Bioinformatics. 2011 Sep 1;27(17):2414-21. doi: 10.1093/bioinformatics/btr414. Epub 2011 Jul 28.
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.
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.
Supplementary data are available at Bioinformatics online.
蛋白质-蛋白质相互作用 (PPI) 数据库是研究细胞途径和网络的常用工具；然而，仍有一些数据库尚未考虑到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，最后研究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。
我们发现，评估的数据库在节点连接性方面（即具有少数相互作用伙伴的蛋白质在其他数据库中也具有少数相互作用伙伴）具有可比性，但相互作用伙伴的身份可能不同。我们还观察到，纳入组织特异性表达信息显著改变了相互作用景观，最后，我们证明了许多研究最深入的蛋白质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，但可能会对相互作用或途径产生不同的预测。通过纳入器官和细胞类型特异性基因表达数据集，并为最“流行”的蛋白质获得额外的相互作用证据，可以提高预测的准确性。
补充数据可在“生物信息学在线”获得。
