Comprehensive study of the exposome and omic data using rexposome Bioconductor Packages

Area of Science:

• Environmental health
• Bioinformatics
• Genomics

Background:

• Genomics advanced disease understanding, but environmental exposures (exposome) also impact health.
• High-dimensional exposome data requires specialized bioinformatics tools for analysis and integration with omic data.

Purpose of the Study:

• To present rexposome project, a novel R/Bioconductor framework for managing, visualizing, and analyzing high-dimensional exposome data.
• To facilitate disease association studies by integrating exposome data with health outcomes and multi-omic layers.

Main Methods:

• Developed a generic framework, rexposome project, using object-oriented classes and methods in R/Bioconductor.
• Integrated exposome data management with disease association analysis and high-throughput omic data integration (transcriptome, methylome).

Main Results:

• Demonstrated the utility of rexposome project using a real-world dataset.
• Successfully analyzed exposome data in conjunction with respiratory disease outcomes and integrated omic data.

Conclusions:

• rexposome project provides a comprehensive solution for exposome data analysis in disease etiology.
• The framework supports the integration of environmental exposures with molecular data for a holistic understanding of health.