Nuclear Receptor Signaling Atlas (NURSA) Informatics, Dan L. Duncan Cancer Center Biomedical Informatics Group, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
NURSA Informatics, Department of Molecular and Cellular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
Sci Signal. 2017 Apr 25;10(476):eaah6275. doi: 10.1126/scisignal.aah6275.
We previously developed a web tool, Transcriptomine, to explore expression profiling data sets involving small-molecule or genetic manipulations of nuclear receptor signaling pathways. We describe advances in biocuration, query interface design, and data visualization that enhance the discovery of uncharacterized biology in these pathways using this tool. Transcriptomine currently contains about 45 million data points encompassing more than 2000 experiments in a reference library of nearly 550 data sets retrieved from public archives and systematically curated. To make the underlying data points more accessible to bench biologists, we classified experimental small molecules and gene manipulations into signaling pathways and experimental tissues and cell lines into physiological systems and organs. Incorporation of these mappings into Transcriptomine enables the user to readily evaluate tissue-specific regulation of gene expression by nuclear receptor signaling pathways. Data points from animal and cell model experiments and from clinical data sets elucidate the roles of nuclear receptor pathways in gene expression events accompanying various normal and pathological cellular processes. In addition, data sets targeting non-nuclear receptor signaling pathways highlight transcriptional cross-talk between nuclear receptors and other signaling pathways. We demonstrate with specific examples how data points that exist in isolation in individual data sets validate each other when connected and made accessible to the user in a single interface. In summary, Transcriptomine allows bench biologists to routinely develop research hypotheses, validate experimental data, or model relationships between signaling pathways, genes, and tissues.
我们之前开发了一个网络工具 Transcriptomine,用于探索涉及核受体信号通路的小分子或遗传操作的表达谱数据集。我们描述了生物注释、查询界面设计和数据可视化方面的进展,这些进展增强了使用该工具在这些途径中发现未被描述的生物学的能力。Transcriptomine 目前包含约 4500 万个数据点,涵盖了近 550 个从公共档案中检索并系统整理的参考库中的 2000 多个实验。为了使基础数据点更容易被实验生物学家使用,我们将实验小分子和基因操作分类为信号通路,将实验组织和细胞系分类为生理系统和器官。将这些映射纳入 Transcriptomine 中,使用户能够轻松评估核受体信号通路对基因表达的组织特异性调节。来自动物和细胞模型实验以及临床数据集的数据点阐明了核受体途径在伴随各种正常和病理细胞过程的基因表达事件中的作用。此外,针对非核受体信号通路的数据集突出了核受体与其他信号通路之间的转录交叉对话。我们通过具体示例展示了如何在单个界面中将孤立存在于单个数据集中的数据点连接起来并提供给用户,从而使它们相互验证。总之,Transcriptomine 使实验生物学家能够常规地提出研究假设、验证实验数据或模拟信号通路、基因和组织之间的关系。