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

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.