Miller Ryan A, Ehrhart Friederike, Eijssen Lars M T, Slenter Denise N, Curfs Leopold M G, Evelo Chris T, Willighagen Egon L, Kutmon Martina
Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands.
GKC-Rett Expertise Centre, MUMC+, Maastricht, Netherlands.
Front Genet. 2019 Feb 21;10:59. doi: 10.3389/fgene.2019.00059. eCollection 2019.
Pathway and network approaches are valuable tools in analysis and interpretation of large complex omics data. Even in the field of rare diseases, like Rett syndrome, omics data are available, and the maximum use of such data requires sophisticated tools for comprehensive analysis and visualization of the results. Pathway analysis with differential gene expression data has proven to be extremely successful in identifying affected processes in disease conditions. In this type of analysis, pathways from different databases like WikiPathways and Reactome are used as separate, independent entities. Here, we show for the first time how these pathway models can be used and integrated into one large network using the WikiPathways RDF containing all human WikiPathways and Reactome pathways, to perform network analysis on transcriptomics data. This network was imported into the network analysis tool Cytoscape to perform active submodule analysis. Using a publicly available Rett syndrome gene expression dataset from frontal and temporal cortex, classical enrichment analysis, including pathway and Gene Ontology analysis, revealed mainly immune response, neuron specific and extracellular matrix processes. Our active module analysis provided a valuable extension of the analysis prominently showing the regulatory mechanism of , especially on DNA maintenance, cell cycle, transcription, and translation. In conclusion, using pathway models for classical enrichment and more advanced network analysis enables a more comprehensive analysis of gene expression data and provides novel results.
通路和网络方法是分析和解释大型复杂组学数据的宝贵工具。即使在雷特综合征等罕见病领域，也有组学数据可用，而要充分利用这些数据，就需要复杂的工具来对结果进行全面分析和可视化。利用差异基因表达数据进行通路分析已被证明在识别疾病状态下受影响的过程方面极为成功。在这类分析中，来自不同数据库（如WikiPathways和Reactome）的通路被用作单独的、独立的实体。在此，我们首次展示了如何使用包含所有人类WikiPathways和Reactome通路的WikiPathways RDF将这些通路模型用于并整合到一个大型网络中，以便对转录组学数据进行网络分析。该网络被导入到网络分析工具Cytoscape中进行活性子模块分析。使用来自额叶和颞叶皮质的公开可用的雷特综合征基因表达数据集，经典富集分析（包括通路分析和基因本体分析）主要揭示了免疫反应、神经元特异性和细胞外基质过程。我们的活性模块分析显著扩展了分析内容，突出显示了尤其是在DNA维持、细胞周期、转录和翻译方面的调控机制。总之，使用通路模型进行经典富集和更高级的网络分析能够对基因表达数据进行更全面的分析并提供新的结果。
