DMRcate

DMRcate detects and characterizes differentially methylated regions (DMRs) across the human genome using Illumina Infinium Array data (450K and EPIC) and whole-genome bisulfite sequencing (WGBS) to identify methylation differences between phenotypes.

Key Features:

• Gaussian kernel smoothing: Applies a Gaussian kernel to spatially fit replicated methylation measurements across the genome, providing robustness to irregular CpG spacing.
• Tunable smoothing and ranking: Uses a tunable kernel bandwidth to smooth the differential methylation signal and rank candidate DMRs.
• Annotation-agnostic detection: Identifies DMRs without reliance on genomic annotation or local changes in signal direction.
• Significance assignment: Assigns statistical significance to each DMR by comparison with a null model.
• Cross-platform validation: Validates array-derived DMRs against WGBS-derived DMRs from the same DNA samples.
• Benchmark performance: Demonstrates superior performance relative to Bumphunter and Probe Lasso and comparable performance to comb-p on simulated and real data.

Scientific Applications:

• Epigenome-wide differential methylation analysis: Detects DMRs between phenotypes using 450K, EPIC, and WGBS data.
• Cross-platform DMR validation: Enables comparison and validation of DMRs identified by array platforms with WGBS from the same samples.
• Functional epigenetics investigations: Supports studies of how methylation patterns influence gene expression and contribute to complex traits and diseases.

Methodology:

Fits replicated methylation measurements spatially using a Gaussian kernel with tunable smoothing, ranks regions by the smoothed differential methylation signal, and assigns significance by comparison to a null model.