在环境卫生方面,将化学暴露与不良终点联系起来的特定分子机制通常是未知的,反映知识差距。在公开的比较毒性基因组学数据库(CTD;https://ctdbase.org/)上,我们集成了手动策划,从CTD到计算四个单位信息块的基于文献的相互作用,这些信息块被组织为潜在的逐步分子机制,被称为“CGPD-四聚体,“其中化学物质与基因产物相互作用以触发可以与疾病相关的表型。这些计算导出的数据集可用于填补空白并提供可测试的机械信息。用户可以生成任何化学组合的CGPD四聚体,基因,表型,和/或CTD感兴趣的疾病;然而,这样的查询通常导致生成数千个CGPD-四聚体。这里,我们描述了一种使用R将这些大型数据集转换为用户友好的和弦图的新方法。这个可视化过程很简单,实现简单,并且从未使用过R的经验不足的用户可以访问。将CGPD-四聚体结合到单个和弦图中有助于识别潜在的关键化学物质,基因,表型,和疾病。这种可视化允许用户更容易地分析计算数据集,这些计算数据集可以填补环境健康连续体中的暴露知识空白。
In environmental health, the specific molecular mechanisms connecting a chemical exposure to an adverse endpoint are often unknown, reflecting knowledge gaps. At the public Comparative Toxicogenomics Database (CTD; https://ctdbase.org/), we integrate manually curated, literature-based interactions from CTD to compute four-unit blocks of information organized as a potential step-wise molecular mechanism, known as \"CGPD-
tetramers,\" wherein a chemical interacts with a gene product to trigger a phenotype which can be linked to a disease. These computationally derived datasets can be used to fill the gaps and offer testable mechanistic information. Users can generate CGPD-
tetramers for any combination of chemical, gene, phenotype, and/or disease of interest at CTD; however, such queries typically result in the generation of thousands of CGPD-
tetramers. Here, we describe a novel approach to transform these large datasets into user-friendly chord diagrams using R. This visualization process is straightforward, simple to implement, and accessible to inexperienced users that have never used R before. Combining CGPD-
tetramers into a single chord diagram helps identify potential key chemicals, genes, phenotypes, and diseases. This visualization allows users to more readily analyze computational datasets that can fill the exposure knowledge gaps in the environmental health continuum.