Wright Sarah N, Colton Scott, Schaffer Leah V, Pillich Rudolf T, Churas Christopher, Pratt Dexter, Ideker Trey
Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
Mol Syst Biol. 2025 Jan;21(1):1-29. doi: 10.1038/s44320-024-00077-y. Epub 2024 Dec 9.
Advancements in genomic and proteomic technologies have powered the creation of large gene and protein networks ("interactomes") for understanding biological systems. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 45 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP, Reactome, and SIGNOR demonstrate stronger performance in interaction prediction. Our study provides a benchmark for interactomes across diverse biological applications and clarifies factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.
基因组学和蛋白质组学技术的进步推动了大型基因和蛋白质网络（“相互作用组”）的创建，以理解生物系统。然而，相互作用组的激增使得为特定应用选择网络变得复杂。在这里，我们对45个当前人类相互作用组进行了全面评估，涵盖蛋白质-蛋白质相互作用以及基因调控、信号传导、共定位和遗传相互作用网络。我们的分析表明，像HumanNet、STRING和FunCoup这样的大型复合网络在识别疾病基因方面最有效，而像DIP、Reactome和SIGNOR这样的较小网络在相互作用预测方面表现更强。我们的研究为跨多种生物学应用的相互作用组提供了一个基准，并阐明了影响网络性能的因素。此外，我们的评估管道为未来持续评估新兴和更新的相互作用网络铺平了道路。
