---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**:
```bash
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