ToxDAR: A Workflow Software for Analyzing Toxicologically Relevant Proteomic and Transcriptomic Data, from Data...

Area of Science:

• Toxicology and Computational Biology
• Multi-omics Data Analysis
• Drug Discovery and Development

Background:

• Understanding toxicological mechanisms is crucial for drug development, chemical safety, and public health.
• High-throughput omics technologies generate vast datasets for studying toxicological responses.
• Challenges exist in analyzing and integrating these omics data with existing toxicological knowledge.

Purpose of the Study:

• To develop a knowledge-based analysis system for toxicological multi-omics data.
• To facilitate the identification of biological pathways associated with chemical toxicity.
• To establish connections between omics data and established toxicological knowledge.

Main Methods:

• Development of ToxDAR, a workflow-oriented R package for toxicological multi-omics data analysis.
• Integration of existing R packages (NormExpression, DESeq2, igraph) and functions (prcomp, phyper).
• Incorporation of a knowledge graph with biological entities and interaction types for data annotation.
• Case study analysis of a triphenyl phosphate (TPP) transcriptomic dataset.

Main Results:

• ToxDAR supports data preparation, quality control, differential expression, functional, and network analysis.
• The knowledge graph provides comprehensive annotation for omics analysis results.
• Analysis of TPP toxicology suggests impairment of thyroid function via thyroid hormone receptor β (THRB) activation, affecting cell death and inflammation pathways.

Conclusions:

• ToxDAR is a valuable tool for understanding toxicological mechanisms from multi-omics data.
• The package aids in discovering novel therapeutic targets and evaluating chemical safety.
• It bridges the gap between complex omics data and actionable toxicological insights.