Toxicogenomics, the application of genomics to toxicology, was described as 'a new era' for toxicology. Standard toxicity tests typically involve a number of short-term bioassays that are costly, time consuming, require large numbers of animals and generally focus on a single end point. Toxicogenomics was heralded as a way to improve the efficiency of toxicity testing by assessing gene regulation across the genome, allowing rapid classification of compounds based on characteristic expression profiles. Gene expression microarrays could measure and characterise genome-wide gene expression changes in a single study and while transcriptomic profiles that can discriminate between genotoxic and non-genotoxic carcinogens have been identified, challenges with the approach limited its application. As such, toxicogenomics did not transform the field of genetic toxicology in the way it was predicted. More recently, next generation sequencing (NGS) technologies have revolutionised genomics owing to the fact that hundreds of billions of base pairs can be sequenced simultaneously cheaper and quicker than traditional Sanger methods. In relation to genetic toxicology, and thousands of cancer genomes have been sequenced with single-base substitution mutational signatures identified, and mutation signatures have been identified following treatment of cells with known or suspected environmental carcinogens. RNAseq has been applied to detect transcriptional changes following treatment with genotoxins; modified RNAseq protocols have been developed to identify adducts in the genome and Duplex sequencing is an example of a technique that has recently been developed to accurately detect mutation. Machine learning, including MutationSeq and SomaticSeq, has also been applied to somatic mutation detection and improvements in automation and/or the application of machine learning algorithms may allow high-throughput mutation sequencing in the future. This review will discuss the initial promise of transcriptomics for genetic toxicology, and how the development of NGS technologies and new machine learning algorithms may finally realise that promise.

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毒理基因组学对遗传毒理学的承诺：过去，现在和未来。

毒理基因组学，即基因组学在毒理学中的应用，被描述为毒理学的“新时代”。标准毒性测试通常涉及许多短期生物测定，这些测定昂贵，费时，需要大量动物并且通常集中在单个终点上。毒理基因组学被认为是通过评估整个基因组的基因调控来提高毒性测试效率的一种方法，从而可以根据特征表达谱对化合物进行快速分类。基因表达微阵列可以在一项研究中测量和表征全基因组范围内基因表达的变化，尽管已经确定了可以区分遗传毒性和非遗传毒性致癌物的转录组谱，但是该方法的挑战限制了其应用。因此，毒理基因组学并没有像预测的那样改变遗传毒理学领域。最近，新一代测序（NGS）技术彻底革新了基因组学，原因是与传统的Sanger方法相比，成千上万的碱基对可以更便宜，更快速地同时测序。关于遗传毒理学，已对数千个癌症基因组进行了测序，鉴定出了单碱基取代突变特征，并在用已知或怀疑的环境致癌物处理细胞后鉴定出突变特征。RNAseq已用于检测基因毒素处理后的转录变化；已开发出改进的RNAseq方案以鉴定基因组中的加合物，而双链测序是最近已开发出的一种可准确检测突变的技术。包括MutationSeq和SomaticSeq在内的机器学习也已应用于体细胞突变检测和自动化方面的改进和/或机器学习算法的应用可能会在将来允许高通量突变测序。这篇综述将讨论转录组学对遗传毒理学的最初希望，以及NGS技术和新的机器学习算法的发展如何最终实现这一希望。还已经将其应用于体细胞突变检测和自动化的改进和/或机器学习算法的应用可能在将来允许高通量突变测序。这篇综述将讨论转录组学对遗传毒理学的最初希望，以及NGS技术和新的机器学习算法的发展如何最终实现这一希望。还已经将其用于体细胞突变检测和自动化的改进和/或机器学习算法的应用可能在将来允许高通量突变测序。这篇综述将讨论转录组学对遗传毒理学的最初希望，以及NGS技术和新的机器学习算法的发展如何最终实现这一希望。