tooluniverse-epigenomics
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,
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-epigenomics --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ha
- When to Use This Skill
- Required Python Packages
- KEY PRINCIPLES
- Complete Workflow
- Phase 0: Question Parsing and Data Discovery
- Phase 1: Methylation Data Processing
- Phase 2: ChIP-seq Peak Analysis
- Phase 3: ATAC-seq Analysis
- Phase 4: Multi-Omics Integration
- Phase 5: Clinical Data Integration
- Phase 6: ToolUniverse Annotation Integration
- Phase 7: Genome-Wide Statistics
- ToolUniverse Tool Parameter Reference
- Regulatory Annotation Tools
What does the tooluniverse-epigenomics skill do?
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,
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-epigenomics --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.