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Related Experiment Video

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Applying the omics data analysis framework for regulatory application (R-ODAF) filtering criteria: A case study with

Yuko Ogata1

  • 1Division of Chemical Information, Research Center for Chemical Information and Management, National Institute of Occupational Safety and Health, 6-21-1 Nagao, Tama-Ku, Kawasaki, Kanagawa, 214-8585, Japan.

Regulatory Toxicology and Pharmacology : RTP
|July 6, 2026
PubMed
Summary

The omics data analysis framework for regulatory application (R-ODAF) was tested on RNA-Seq data. The false discovery rate filter removed the most genes, but these genes still aided in interpreting chemical effects.

Keywords:
3-MethylcholanthreneFiltering criteriaInformation entropyR-ODAFRNA-SeqTK6 cellsTranscriptome

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Area of Science:

  • Toxicogenomics
  • Chemical Risk Assessment
  • Transcriptomics

Background:

  • The omics data analysis framework for regulatory application (R-ODAF) aims to standardize toxicogenomic data use in chemical risk assessments.
  • Limited studies have evaluated the practical applicability of R-ODAF.

Purpose of the Study:

  • To assess the R-ODAF's applicability by applying its filtering criteria to RNA-Seq data.
  • To investigate the impact of 3-methylcholanthrene (3-MCA) exposure on gene expression in TK6 cells using R-ODAF.

Main Methods:

  • Applied R-ODAF filtering criteria to RNA-Seq data from TK6 cells exposed to 3-MCA.
  • Utilized false discovery rate (FDR) filtering for differentially expressed genes (DEGs).
  • Measured transcriptome information entropy to detect biologically significant changes.

Main Results:

  • All samples met the R-ODAF processing criteria.
  • The FDR filter removed the largest number of DEGs.
  • Pathway and literature analyses of FDR-discarded genes supported the interpretation of 3-MCA's effects on gene expression.

Conclusions:

  • The R-ODAF framework is applicable for analyzing toxicogenomic data.
  • The FDR filter's stringency may lead to the exclusion of biologically relevant genes.
  • Further investigation into gene selection criteria within R-ODAF is warranted.