University of Cambridge, Centre for Molecular Informatics, Department of Chemistry, Lensfield Road, Cambridge CB2 1EW, UK.
Mol Omics. 2018 Aug 6;14(4):218-236. doi: 10.1039/c8mo00042e.
The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.
毒理基因组学领域旨在通过使用“组学”数据来理解和预测毒性，以便研究系统对化合物处理的反应。近年来，毒理学和“组学”数据（特别是基因表达数据）的公开可用性迅速增加，相应地也开发了用于分析这些数据的方法。在这篇综述中，我们总结了与 RNA-Seq 和微阵列数据分析相关的最新进展，回顾了相关数据库，并强调了毒理基因组学数据在理解和预测化合物毒性方面的最新应用。这些应用包括差异表达基因及其富集分析、特征匹配、基于相互作用网络的方法以及共表达网络分析。未来，这些最先进的方法可能会与新技术（如全人体模型）相结合，从而全面了解毒性，减少在动物模型中进行体内毒性评估的必要性。
