Towards the development of an omics data analysis framework

M. Verheijen, W.D. Tong, L.M. Shi, T.W. Gant, B. Seligman, F. Caiment*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.
Original languageEnglish
Article number104621
Number of pages4
JournalRegulatory Toxicology and Pharmacology
Publication statusPublished - 1 Apr 2020


  • data-analysis framework
  • future
  • regulatory agencies
  • reproducibility
  • rna-seq
  • toxicogenomics
  • toxicology
  • transcriptomics
  • Regulatory agencies
  • Toxicogenomics
  • Data-analysis framework
  • Toxicology
  • Transcriptomics


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