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Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data
Molecular Omics ( IF 2.9 ) Pub Date : 2018-06-19 , DOI: 10.1039/c8mo00042e
Benjamin Alexander-Dann 1, 2, 3, 4, 5 , Lavinia Lorena Pruteanu 1, 2, 3, 4, 5 , Erin Oerton 1, 2, 3, 4, 5 , Nitin Sharma 1, 2, 3, 4, 5 , Ioana Berindan-Neagoe 6, 6, 7, 8, 9 , Dezső Módos 1, 2, 3, 4, 5 , Andreas Bender 1, 2, 3, 4, 5
Affiliation  

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.

中文翻译:

毒物基因组学的发展:从基因表达数据理解和预测化合物诱导的毒性

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,以研究系统对复合疗法的反应。近年来,公开获得的毒理学和“组学”数据(尤其是基因表达数据)和其分析方法的相应发展迅速增加。在这篇综述中,我们总结了与RNA-Seq和微阵列数据分析有关的最新进展,回顾了相关数据库,并着重介绍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。这些包括差异表达基因及其富集的分析,签名匹配,基于相互作用网络的方法以及共表达网络的分析。将来,动物模型中的体内毒性评估。
更新日期:2018-12-01
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