Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.
Department of Pathology and Cancer Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
Nat Protoc. 2016 Oct;11(10):1889-907. doi: 10.1038/nprot.2016.117. Epub 2016 Sep 8.
ConsensusPathDB consists of a comprehensive collection of human (as well as mouse and yeast) molecular interaction data integrated from 32 different public repositories and a web interface featuring a set of computational methods and visualization tools to explore these data. This protocol describes the use of ConsensusPathDB (http://consensuspathdb.org) with respect to the functional and network-based characterization of biomolecules (genes, proteins and metabolites) that are submitted to the system either as a priority list or together with associated experimental data such as RNA-seq. The tool reports interaction network modules, biochemical pathways and functional information that are significantly enriched by the user's input, applying computational methods for statistical over-representation, enrichment and graph analysis. The results of this protocol can be observed within a few minutes, even with genome-wide data. The resulting network associations can be used to interpret high-throughput data mechanistically, to characterize and prioritize biomarkers, to integrate different omics levels, to design follow-up functional assay experiments and to generate topology for kinetic models at different scales.
ConsensusPathDB 包含了一个全面的人类(以及小鼠和酵母)分子相互作用数据集合,这些数据是从 32 个不同的公共数据库中整合而来的,并且还提供了一个网络界面,其中包含了一系列计算方法和可视化工具,用于探索这些数据。本协议描述了 ConsensusPathDB(http://consensuspathdb.org)的使用方法,涉及到提交给系统的生物分子(基因、蛋白质和代谢物)的功能和网络特征化,这些分子可以作为优先级列表提交,也可以与相关的实验数据(如 RNA-seq)一起提交。该工具报告了交互网络模块、生化途径和功能信息,这些信息是由用户输入的内容显著富集而来的,应用了计算方法进行统计过表达、富集和图形分析。该协议的结果可以在几分钟内观察到,即使是对于全基因组数据也是如此。由此产生的网络关联可以用于从机制上解释高通量数据,用于特征化和优先考虑生物标志物,用于整合不同的组学水平,用于设计后续的功能测定实验,并为不同尺度的动力学模型生成拓扑结构。