---name: epigenomics-methylgpt-agent description: AI-powered DNA methylation analysis using MethylGPT foundation models for epigenomic profiling, differential methylation detection, and cancer epigenome characterization. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:

• system: Python 3.10+ allowed-tools:
• run_shell_command
• read_file
• write_file

keywords:

• epigenomics-methylgpt-agent
• automation
• biomedical measurable_outcome: execute task with >95% success rate. ---"

Epigenomics MethylGPT Agent

The Epigenomics MethylGPT Agent leverages foundation models for comprehensive DNA methylation analysis. It integrates MethylGPT and DiffuCpG for methylation profiling, differential methylation region (DMR) detection, and cancer epigenome characterization at single-base resolution.

When to Use This Skill

• When analyzing whole-genome bisulfite sequencing (WGBS) data for methylation patterns.
• To identify differentially methylated regions (DMRs) between conditions (e.g., tumor vs. normal).
• For cancer epigenome profiling and epigenetic biomarker discovery.
• When predicting CpG methylation states using deep learning models.
• To impute missing methylation data in high-throughput studies.

Core Capabilities

1. MethylGPT Foundation Model: Leverages transformer-based architecture trained on large-scale methylome data for methylation state prediction and pattern recognition.

2. Differential Methylation Analysis: Identifies DMRs with increased sensitivity using AI-enhanced detection compared to traditional statistical methods.

3. Cancer Epigenome Profiling: Specialized analysis for tumor methylation signatures, including hypermethylation of tumor suppressors and global hypomethylation patterns.

4. Missing Data Imputation: Uses DiffuCpG generative AI model to address missing data in methylation arrays and sequencing studies.

5. Single-Base Resolution: Deep learning models capture sequence context and long-range dependencies for accurate CpG methylation identification.

6. Multi-Platform Support: Analyzes data from Illumina methylation arrays (450K, EPIC), WGBS, RRBS, and targeted bisulfite sequencing.

Workflow

1. Input: Provide methylation data (beta values, WGBS BAM files, or raw intensity data) and sample metadata.

2. Preprocessing: Quality control, normalization, and batch effect correction.

3. Analysis: Apply MethylGPT for methylation prediction, DMR calling, and pattern discovery.

4. Interpretation: Annotate DMRs to genomic features (promoters, enhancers, gene bodies) and pathways.

5. Output: DMR reports, methylation heatmaps, pathway enrichment, and epigenetic age estimates.

Example Usage

User: "Identify differentially methylated regions between tumor and normal samples in this WGBS dataset."

Agent Action:

python3 Skills/Genomics/Epigenomics_MethylGPT_Agent/methylgpt_analyzer.py \
    --input tumor_normal_methylation.csv \
    --groups tumor,normal \
    --model methylgpt-base \
    --analysis dmr \
    --min_cpgs 5 \
    --delta_beta 0.2 \
    --output dmr_results.json

Key Methods and Tools

| Method | Application | Reference | |--------|-------------|-----------| | MethylGPT | Foundation model for methylome analysis | 2025 Nature Methods | | DiffuCpG | Generative AI for missing data imputation | 2025 Bioinformatics | | DeepMethyl | WGBS analysis for DMR detection | 2024 Genome Biology | | minfi | Illumina array preprocessing | Bioconductor | | DSS | Statistical DMR calling | Bioconductor |

Prerequisites

• Python 3.10+
• PyTorch 2.0+
• Transformers library
• methylgpt-model weights
• Bioconductor R packages (optional)

Related Skills

• Single_Cell_Foundation_Models - For single-cell methylation analysis
• Variant_Interpretation - For methylation-variant associations
• Multi_Omics_Integration - For combining methylation with expression data

Methodology

DNA methylation analysis leverages CNNs and transformers to capture sequence context and long-range dependencies. The MethylGPT foundation model is pre-trained on millions of CpG sites across diverse tissues and conditions, enabling transfer learning for specific applications. DiffuCpG uses diffusion-based generative modeling to impute missing methylation values while preserving biological structure.

Author

AI Group - Biomedical AI Platform