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使用 ConsensusPathDB 在网络层面分析和解释基因组数据。

Analyzing and interpreting genome data at the network level with ConsensusPathDB.

机构信息

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.

出版信息

Nat Protoc. 2016 Oct;11(10):1889-907. doi: 10.1038/nprot.2016.117. Epub 2016 Sep 8.

Abstract

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.

摘要

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。

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