使用转录组数据和基于关键特征的基因组评估人类健康危害的工作流程
Han-Hsuan D Tsai1,2, King David Oware3, Fred A Wright1,4,5
1Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas, 77843, USA.
概括
本研究引入了一种使用关键特征 (KCs) 的新计算方法,以改进用于化学危害识别的转录组数据的解释. 通过将KC映射到基因组中,研究人员可以更好地了解化学毒性机制.
科学领域:
- 毒素基因组学 毒素基因组学
- 计算生物学 计算生物学
- 化学危险识别 化学危险识别
背景情况:
- 用于危险识别的转录学数据分析面临着由于路径冗余和复杂解释的挑战.
- 关键特征 (KCs) 对于理解化学品对健康的危害至关重要,但需要与转录基因数据进行更好的整合.
研究的目的:
- 开发和验证一种用于与基因组交叉映射KC的计算方法,以提高毒基因组数据的解释性.
- 改进化学危险识别的系统审查过程,使用集成的转录和KC数据.
主要方法:
- 总结了72个KC成34个总体术语,并映射了来自Reactome和KEGG的基因组,以创建"KC基因组".
- 通过使用公开的,二氧化物,苏尼提尼和阿莫西西林的转录基因数据集评估了KC基因组的性能.
- 评估了与已知的化学机制和毒性相关的KC术语的丰富性.
主要成果:
- KC基因组显示了Reactome和KEGG映射之间的最小重叠和互补性.
- 丰富分析成功地确定了与测试毒素 (,二氧化物,苏尼提尼布) 的机制相关的KC术语.
- 负对照 (阿莫西西林) 显示出有限且边际显著的丰富度,验证了方法的特异性.
结论:
- 与基因组交叉映射KC提供了一个强大的计算方法来改善毒基因组数据的解释.
- 这种方法有助于更透明和系统地识别化学品的危险.
- 开发的KC基因组是监管科学和毒理学研究的宝贵工具.
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