MethReg

MethReg models the influence of CpG DNA methylation on transcription factor (TF)-mediated gene expression by integrating matched DNA methylation and gene-expression data in an R/Bioconductor framework to prioritize regulatory CpG sites identified in epigenome-wide association studies (EWAS).

Key Features:

• Integration of multi-omics data: Analyzes matched DNA methylation and gene-expression datasets and incorporates external transcription factor (TF) binding information.
• Simultaneous modeling approach: Employs a simultaneous modeling strategy that jointly considers CpG methylation levels, target gene expression, and TF activity to detect methylation-dependent regulatory associations.
• Prioritization and annotation: Prioritizes and annotates CpG sites with high regulatory potential based on their impact on TF-target gene associations.
• Demonstrated on disease datasets: Applied to colorectal cancer and Alzheimer's disease datasets to reveal methylation-dependent regulatory mechanisms.

Scientific Applications:

• EWAS prioritization: Prioritizes and annotates distal regulatory CpGs from epigenome-wide association studies for functional follow-up.
• Complex disease epigenomics: Dissects methylation-dependent TF-target interactions in colorectal cancer and Alzheimer's disease datasets.
• Subgroup-specific regulation discovery: Identifies TF-target gene associations specific to sample subsets with distinct methylation profiles.

Methodology:

Integrates matched DNA methylation and gene-expression datasets, incorporates external TF binding information, and applies a simultaneous modeling strategy that considers CpG methylation levels, target gene expression, and TF activity to identify methylation-dependent TF-target associations and prioritize regulatory CpGs.