The use of various omics techniques for scientific research is increasing. While toxicogenomics studies have already produced substantial data on diverse omics platforms, to date there has been little routine application in regulatory toxicology. This is despite the promises and excitement of 20 years ago when it was widely speculated that omics methods would reduce or even replace animal use and allow a much enhanced understanding of hazard and susceptibility. One of the reasons for this has been a trepidation about relying on the produced data. It has been argued that omics outputs might not be sufficiently reliable for regulatory application because the techniques, bioinformatics and interpretation can vary. For these reasons the robustness of the obtained results is questioned. This reticence to trust omics data is further magnified by the lack of internationally agreed upon guidelines and protocols for both the generation and processing of omics data. One way forward would be to reach a consensus on an omics data analysis framework (ODAF) for regulatory application (R-ODAF) based on rigorous data analysis. The authors of this article are involved in a Long-Range Research Initiative (LRI) project that will propose an R-ODAF for transcriptomics data. The R-ODAF will then be reviewed and evaluated by the main regulatory agencies and consensus forums such as the Organization for Economic Co-operation and Development (OECD). This work builds on The MicroArray Quality Control work that developed standards for the generation of data from microarrays and sequencing but not for reporting or analysis.

中文翻译：

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致力于组学数据分析框架的开发。

各种组学技术在科学研究中的使用正在增加。尽管毒理基因组学研究已经在各种毒理学平台上获得了大量数据，但迄今为止，在调节毒理学中几乎没有常规应用。尽管有20年前的希望和兴奋，当时人们普遍认为，组学方法将减少甚至取代动物的使用，并使人们对危害和易感性有了更多的了解。原因之一是对依赖于产生的数据感到不安。有人认为，由于技术，生物信息学和解释可能会有所不同，因此组学输出对于监管应用可能不够可靠。由于这些原因，对所得结果的鲁棒性提出了质疑。由于缺乏有关组学数据生成和处理的国际公认准则和协议，对信任组学数据的沉默进一步扩大了。一种前进的方法是在基于严格数据分析的法规应用的组学数据分析框架（ODAF）（R-ODAF）上达成共识。本文的作者参与了一项远程研究计划（LRI）项目，该项目将为转录组学数据提出R-ODAF。然后，主要的监管机构和共识论坛（如经济合作与发展组织（OECD））将对R-ODAF进行审查和评估。这项工作建立在微阵列质量控制工作的基础上，该工作开发了用于从微阵列生成数据和测序的标准，而不是用于报告或分析的标准。