rexposome

rexposome provides R/Bioconductor-based object-oriented classes and methods to manage, visualize, and analyze high-dimensional exposome data and integrate it with multi-omics (transcriptomics, methylomics) to investigate associations between environmental exposures and health outcomes.

Key Features:

• R/Bioconductor framework: Implements functionality within the R/Bioconductor architecture.
• Object-oriented classes and methods: Uses object-oriented classes and methods to represent and operate on exposome and omics data.
• Data management: Manages high-dimensional exposome datasets in conjunction with omic layers including transcriptomics and methylomics.
• Visualization and analysis: Provides methods to visualize and analyze exposome and multi-omics data.
• Multi-omics support: Integrates exposome data with methylomes, transcriptomes, proteomic, and metabolomic profiles.
• Exposure coverage: Handles datasets comprising over 100 chemical, outdoor, social, and lifestyle exposures assessed during pregnancy and childhood.
• Exposome–health integration: Enables integration of exposome data with health outcomes to identify environmental contributions to disease etiology.
• Association detection: Identifies exposome-omics associations, including prenatal exposure links (e.g., maternal smoking, cadmium, molybdenum) with DNA methylation and childhood exposure correlations with the serum metabolome.

Scientific Applications:

• Environmental epidemiology: Investigates environmental contributions to complex disease etiology by integrating exposome and omics data.
• HELIX project analyses: Associates individual exposomes from the Human Early Life Exposome (HELIX) project with multi-omics profiles including methylomes, transcriptomes, proteins, and metabolites.
• Respiratory health studies: Applied to analyze exposome data in relation to respiratory health outcomes integrated with transcriptomic and methylomic profiles.
• Prenatal exposure epigenetics: Detects associations between prenatal exposures (maternal smoking, cadmium, molybdenum) and DNA methylation changes in children.
• Childhood exposure metabolomics: Characterizes childhood exposure associations with the serum metabolome indicating dietary influences, toxic chemicals, essential trace elements, and weather-related effects.

Methodology:

Implements R/Bioconductor object-oriented classes and methods to manage, visualize, and analyze high-dimensional exposome data in conjunction with transcriptomic, methylomic, proteomic, and metabolomic profiles.