epigenomics-methylgpt-agent

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: