Verheijen Marcha Ct, Meier Matthew J, Asensio Juan Ochoteco, Gant Timothy W, Tong Weida, Yauk Carole L, Caiment Florian
Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, the Netherlands.
Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada.
Regul Toxicol Pharmacol. 2022 Jun;131:105143. doi: 10.1016/j.yrtph.2022.105143. Epub 2022 Mar 3.
Despite the widespread use of transcriptomics technologies in toxicology research, acceptance of the data by regulatory agencies to support the hazard assessment is still limited. Fundamental issues contributing to this are the lack of reproducibility in transcriptomics data analysis arising from variance in the methods used to generate data and differences in the data processing. While research applications are flexible in the way the data are generated and interpreted, this is not the case for regulatory applications where an unambiguous answer, possibly later subject to legal scrutiny, is required. A reference analysis framework would give greater credibility to the data and allow the practitioners to justify their use of an alternative bioinformatic process by referring to a standard. In this publication, we propose a method called omics data analysis framework for regulatory application (R-ODAF), which has been built as a user-friendly pipeline to analyze raw transcriptomics data from microarray and next-generation sequencing. In the R-ODAF, we also propose additional statistical steps to remove the number of false positives obtained from standard data analysis pipelines for RNA-sequencing. We illustrate the added value of R-ODAF, compared to a standard workflow, using a typical toxicogenomics dataset of hepatocytes exposed to paracetamol.
尽管转录组学技术在毒理学研究中得到了广泛应用，但监管机构对这些数据用于支持危害评估的接受程度仍然有限。造成这种情况的根本问题在于，转录组学数据分析缺乏可重复性，这源于数据生成方法的差异以及数据处理的不同。虽然研究应用在数据生成和解释方式上较为灵活，但监管应用并非如此，监管应用需要一个明确的答案，可能随后还要接受法律审查。一个参考分析框架将使数据更具可信度，并允许从业者通过参考标准来证明他们使用替代生物信息学流程的合理性。在本出版物中，我们提出了一种名为监管应用组学数据分析框架（R-ODAF）的方法，它被构建为一个用户友好的流程，用于分析来自微阵列和下一代测序的原始转录组学数据。在R-ODAF中，我们还提出了额外的统计步骤，以减少从RNA测序标准数据分析流程中获得的假阳性数量。我们使用一个典型的对乙酰氨基酚暴露肝细胞毒理基因组学数据集，说明了与标准工作流程相比，R-ODAF的附加价值。
