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Practical Application of Toxicogenomics for Profiling Toxicant-Induced Biological Perturbations

机译:毒物基因组学在毒物诱导的生物扰动分析中的实际应用

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A systems-level understanding of molecular perturbations is crucial for evaluating chemical-induced toxicity risks appropriately, and for this purpose comprehensive gene expression analysis or toxicogenomics investigation is highly advantageous. The recent accumulation of toxicity-associated gene sets (toxicogenomic biomarkers), enrichment in public or commercial large-scale microarray database and availability of open-source software resources facilitate our utilization of the toxicogenomic data. However, toxicologists, who are usually not experts in computational sciences, tend to be overwhelmed by the gigantic amount of data. In this paper we present practical applications of toxicogenomics by utilizing biomarker gene sets and a simple scoring method by which overall gene set-level expression changes can be evaluated efficiently. Results from the gene set-level analysis are not only an easy interpretation of toxicological significance compared with individual gene-level profiling, but also are thought to be suitable for cross-platform or cross-institutional toxicogenomics data analysis. Enrichment in toxicogenomics databases, refinements of biomarker gene sets and scoring algorithms and the development of user-friendly integrative software will lead to better evaluation of toxicant-elicited biological perturbations.
机译:对分子扰动的系统级理解对于适当评估化学诱导的毒性风险至关重要,为此,全面的基因表达分析或毒理基因组学研究非常有利。毒性相关基因集(毒性基因组生物标志物)的最新积累,公共或商业大规模微阵列数据库的丰富以及开放源代码软件资源的可用性,促进了我们对毒性基因组学数据的利用。但是,通常不是计算科学专家的毒理学家往往对庞大的数据量感到不知所措。在本文中,我们介绍了利用生物标志物基因组和简单评分方法可以有效评估总体基因组水平表达变化的毒理基因组学的实际应用。与单个基因水平分析相比,基因组水平分析的结果不仅是毒理学意义的简单解释,而且被认为适合于跨平台或跨机构的毒理基因组学数据分析。毒理基因组学数据库的丰富化,生物标志物基因集的完善和评分算法以及用户友好型集成软件的开发,将可以更好地评估由毒物引起的生物扰动。

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