MetaMeth

MetaMeth performs meta-analysis of genome-wide DNA methylation in human skeletal muscle to identify age-related differentially methylated regions and integrate those results with transcriptomic and proteomic data to study epigenetic changes with aging.

Key Features:

• EWAS meta-analysis: Integrates data from 10 studies comprising a total of 908 human muscle methylomes.
• DMR identification: Identifies 9,986 differentially methylated regions at a false discovery rate < 0.005, covering 8,748 unique genes.
• Genome-wide methylation profiling: Analyzes genome-wide DNA methylation patterns specifically in human skeletal muscle.
• Multi-omic integration: Combines DNA methylation results with transcriptomic and proteomic datasets to compare methylation with mRNA and protein-level changes.
• Functional enrichment: Detects significant enrichment for genes associated with skeletal muscle structure and development and for genes showing age-related differential expression.
• Observed methylation–expression discordance: Reports that most differentially methylated genes do not exhibit corresponding alterations at the mRNA or protein level.

Scientific Applications:

• Age-related epigenetic characterization: Characterizes age-associated DNA methylation changes in human skeletal muscle at genome-wide scale.
• Gene and region prioritization: Identifies and prioritizes differentially methylated regions and associated genes for studies of muscle aging.
• Cross-omics comparison: Enables comparison of methylation patterns with transcriptomic and proteomic age-related changes to assess concordance and enrichment.
• Mechanistic investigation: Supports investigation of molecular mechanisms underlying skeletal muscle aging through integrated epigenetic and expression data.

Methodology:

Performs an epigenome-wide association study (EWAS) meta-analysis across 10 studies (908 muscle methylomes), identifies differentially methylated regions with an FDR threshold of < 0.005, and integrates EWAS results with transcriptomic and proteomic datasets.