tooluniverse-epigenomics

Genomics and Epigenomics Data Processing

Production-ready computational skill for processing and analyzing epigenomics data. Combines local Python computation (pandas, scipy, numpy, pysam, statsmodels) with ToolUniverse annotation tools for regulatory context. Designed to solve BixBench-style questions about methylation, ChIP-seq, ATAC-seq, and multi-omics integration.

When to Use This Skill

Triggers:

• User provides methylation data (beta-value matrices, Illumina arrays) and asks about CpG sites
• Questions about differential methylation analysis
• Age-related CpG detection or epigenetic clock questions
• Chromosome-level methylation density or statistics
• ChIP-seq peak files (BED format) with analysis questions
• ATAC-seq chromatin accessibility questions
• Multi-omics integration (expression + methylation, expression + ChIP-seq)
• Genome-wide epigenomic statistics
• Questions mentioning "methylation", "CpG", "ChIP-seq", "ATAC-seq", "histone", "chromatin", "epigenetic"
• Questions about missing data across clinical/genomic/epigenomic modalities
• Regulatory element annotation for processed epigenomic data

Example Questions This Skill Solves:

1. "How many patients have no missing data for vital status, gene expression, and methylation data?"
2. "What is the ratio of filtered age-related CpG density between chromosomes?"
3. "What is the genome-wide average chromosomal density of unique age-related CpGs per base pair?"
4. "How many CpG sites show significant differential methylation (padj < 0.05)?"
5. "What is the Pearson correlation between methylation and expression for gene X?"
6. "How many ChIP-seq peaks overlap with promoter regions?"
7. "What fraction of ATAC-seq peaks are in enhancer regions?"
8. "Which chromosome has the highest density of hypermethylated CpGs?"
9. "Filter CpG sites by variance > threshold and map to nearest genes"
10. "What is the average beta value difference between tumor and normal for chromosome 17?"

NOT for (use other skills instead):

• Gene regulation lookup without data files -> Use existing epigenomics annotation pattern
• RNA-seq differential expression -> Use tooluniverse-rnaseq-deseq2
• Variant calling/annotation from VCF -> Use tooluniverse-variant-analysis
• Gene enrichment analysis -> Use tooluniverse-gene-enrichment
• Protein structure analysis -> Use tooluniverse-protein-structure-retrieval