Production-ready genomics and epigenomics data processing for BixBench questions. Handles methylation array analysis (CpG filtering, differential methylation, age-related CpG detection, chromosome-level density), ChIP-seq peak analysis (peak calling, motif enrichment, coverage stats), ATAC-seq chromatin accessibility, multi-omics integration (expression + methylation correlation), and genome-wide statistics. Pure Python computation (pandas, scipy, numpy, pysam, statsmodels) plus ToolUniverse annotation tools (Ensembl, ENCODE, SCREEN, JASPAR, ReMap, RegulomeDB, ChIPAtlas). Supports BED, BigWig, methylation beta-value matrices, Illumina manifest files, and multi-sample clinical data. Use when processing methylation data, ChIP-seq peaks, ATAC-seq signals, or answering questions about CpG sites, differential methylation, chromatin accessibility, histone marks, or epigenomic statistics.
Production-ready skill combining Python computation (pandas, scipy, numpy, pysam, statsmodels) with ToolUniverse annotation tools for epigenomics analysis.
When uncertain about any scientific fact, SEARCH databases first.
Methylation data, ChIP-seq peaks, ATAC-seq, multi-omics integration, genome-wide epigenomic statistics. Keywords: methylation, CpG, ChIP-seq, ATAC-seq, histone, chromatin, epigenetic.
NOT for: RNA-seq DEG, variant calling, gene enrichment, protein structure.
Identify data files, specific statistic, thresholds, genome build. Categorize by keywords.
See ANALYSIS_PROCEDURES.md for decision tree.
ENCODE tools:
ENCODE_search_rnaseq_experiments: assay_type ("total RNA-seq" default; fall back to "polyA plus RNA-seq"), biosample, limitENCODE_search_histone_experiments: target (e.g., "H3K27ac"), cell_type/tissue/biosample, limitGEO tools: GEO_search_rnaseq_datasets, GEO_search_atacseq_datasets -- both accept limit or max_results
GTEx tools:
GTEx_get_median_gene_expression: gene_symbol (NOT Ensembl ID)GTEx_query_eqtl: gene_symbol, tissue_id (case-sensitive exact, e.g., "Whole_Blood")Other: ensembl_lookup_gene (requires species='homo_sapiens'), ensembl_get_regulatory_features (NO "chr" prefix), SCREEN_get_regulatory_elements, ChIPAtlas_* (requires operation param), SRA_search_experiments (library_strategy: "ChIP-Seq"/"Bisulfite-Seq"/"ATAC-seq")
Global mean/median beta, probe variance, chromosome density, DMP counts.
See CODE_REFERENCE.md for full implementations.
| Pattern | Key Steps |
|---|---|
| Differential methylation | Filter probes → groups → t-test → FDR → threshold |
| Age-related CpG density | Correlate with age → FDR → map to chr → density ratio |
| Multi-omics missing data | Extract IDs → intersect → check NaN → complete case count |
| ChIP-seq annotation | Load peaks → annotate genes → classify regions |
| Methylation-expression | Align samples → correlate → FDR → anti-correlations |
Whole_Blood, Liver, Lung, Breast_Mammary_Tissue, Brain_Cortex, Heart_Left_Ventricle, Kidney_Cortex, Thyroid, Adipose_Subcutaneous, Muscle_Skeletal
| Grade | Criteria |
|---|---|
| Strong | padj < 0.01 AND abs(delta-beta) >= 0.2, replicated |
| Moderate | padj < 0.05 AND abs(delta-beta) >= 0.1 |
| Weak | padj < 0.05 but delta-beta < 0.1 |
| Insufficient | padj >= 0.05 or no replication |
Delta-beta >= 0.2 = strong effect. ChIP-seq: q < 0.01, FE >= 2 for confidence. ATAC-seq NFR < 150bp = active regulatory. Always apply BH FDR. Verify genome build consistency.
CODE_REFERENCE.md, TOOLS_REFERENCE.md, ANALYSIS_PROCEDURES.md, QUICK_START.md