Garmhausen Marius, Hofmann Falko, Senderov Viktor, Thomas Maria, Kandel Benjamin A, Habermann Bianca Hermine
CECAD Research Center, Joseph-Stelzmann-Str. 26, 50931, Cologne, Germany.
Gregor Mendel Institute of Molecular Plant Biology, Austrian Acacdemy of Sciences, Vienna Biocenter (VBC), Dr. Bohr-Gasse 3, 1030, Vienna, Austria.
BMC Genomics. 2015 Oct 14;16:790. doi: 10.1186/s12864-015-2017-z.
Interpreting large-scale studies from microarrays or next-generation sequencing for further experimental testing remains one of the major challenges in quantitative biology. Combining expression with physical or genetic interaction data has already been successfully applied to enhance knowledge from all types of high-throughput studies. Yet, toolboxes for navigating and understanding even small gene or protein networks are poorly developed.
We introduce two Cytoscape plug-ins, which support the generation and interpretation of experiment-based interaction networks. The virtual pathway explorer viPEr creates so-called focus networks by joining a list of experimentally determined genes with the interactome of a specific organism. viPEr calculates all paths between two or more user-selected nodes, or explores the neighborhood of a single selected node. Numerical values from expression studies assigned to the nodes serve to score identified paths. The pathway enrichment analysis tool PEANuT annotates networks with pathway information from various sources and calculates enriched pathways between a focus and a background network. Using time series expression data of atorvastatin treated primary hepatocytes from six patients, we demonstrate the handling and applicability of viPEr and PEANuT. Based on our investigations using viPEr and PEANuT, we suggest a role of the FoxA1/A2/A3 transcriptional network in the cellular response to atorvastatin treatment. Moreover, we find an enrichment of metabolic and cancer pathways in the Fox transcriptional network and demonstrate a patient-specific reaction to the drug.
The Cytoscape plug-in viPEr integrates -omics data with interactome data. It supports the interpretation and navigation of large-scale datasets by creating focus networks, facilitating mechanistic predictions from -omics studies. PEANuT provides an up-front method to identify underlying biological principles by calculating enriched pathways in focus networks.
解读来自微阵列或新一代测序的大规模研究以进行进一步的实验测试仍然是定量生物学中的主要挑战之一。将表达数据与物理或遗传相互作用数据相结合已成功应用于增强各类高通量研究的知识。然而，用于浏览和理解即使是小型基因或蛋白质网络的工具箱仍未得到充分开发。
我们引入了两个Cytoscape插件，它们支持基于实验的相互作用网络的生成和解读。虚拟通路探索器viPEr通过将一组实验确定的基因与特定生物体的相互作用组相结合来创建所谓的焦点网络。viPEr计算两个或更多用户选择节点之间的所有路径，或探索单个选定节点的邻域。分配给节点的表达研究数值用于对识别出的路径进行评分。通路富集分析工具PEANuT用来自各种来源的通路信息注释网络，并计算焦点网络和背景网络之间的富集通路。使用来自六名患者的阿托伐他汀处理的原代肝细胞的时间序列表达数据，我们展示了viPEr和PEANuT的操作和适用性。基于我们使用viPEr和PEANuT的研究，我们提出FoxA1/A2/A3转录网络在细胞对阿托伐他汀治疗的反应中的作用。此外，我们发现Fox转录网络中代谢和癌症通路的富集，并证明了患者对该药物的特异性反应。
Cytoscape插件viPEr将组学数据与相互作用组数据整合在一起。它通过创建焦点网络支持大规模数据集的解读和浏览，促进来自组学研究的机制预测。PEANuT提供了一种通过计算焦点网络中的富集通路来识别潜在生物学原理的前期方法。
