tooluniverse-epigenomics▌
mims-harvard/tooluniverse · updated Apr 8, 2026
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Production-ready skill combining Python computation (pandas, scipy, numpy, pysam, statsmodels) with ToolUniverse annotation tools for epigenomics analysis.
Genomics and Epigenomics Data Processing
Production-ready skill combining Python computation (pandas, scipy, numpy, pysam, statsmodels) with ToolUniverse annotation tools for epigenomics analysis.
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first.
When to Use
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.
Key Principles
- Data-first - Load/inspect before analysis
- Question-driven - Extract specific numeric answer
- Coordinate system awareness - Track genome build (hg19/hg38/mm10), chr prefix
- Statistical rigor - FDR correction, effect size filtering
- CpG identification - Parse Illumina probe IDs, genomic coordinates
Workflow
Phase 0: Question Parsing
Identify data files, specific statistic, thresholds, genome build. Categorize by keywords.
See ANALYSIS_PROCEDURES.md for decision tree.
Phase 1: Methylation Processing
- Load beta/M-value matrix (CSV/TSV/parquet/HDF5)
- Filter by variance, missing rate, probe type, chromosome, CpG island relation
- Differential methylation: T-test/Wilcoxon between groups + FDR
- Age-related CpG: Pearson/Spearman correlation + FDR
- Chromosome density: CpG count / chromosome length
Phase 2: ChIP-seq Peak Analysis
- Load BED/narrowPeak/broadPeak, normalize chromosomes
- Peak stats, annotation to genes, overlap analysis (Jaccard)
Phase 3: ATAC-seq
- NFR detection (<150bp peaks), region classification
Phase 4: Multi-Omics Integration
- Methylation-expression correlation per probe-gene (Pearson/Spearman + FDR)
- ChIP-seq + expression: promoter peaks vs expression levels
Phase 5: Clinical Data
- Missing data analysis across modalities, complete case identification
Phase 6: ToolUniverse Annotation
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,limit
GEO 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")
Phase 7: Genome-Wide Statistics
Global mean/median beta, probe variance, chromosome density, DMP counts.
See CODE_REFERENCE.md for full implementations.
Common Patterns
| 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 |
GTEx Tissue IDs
Whole_Blood, Liver, Lung, Breast_Mammary_Tissue, Brain_Cortex, Heart_Left_Ventricle, Kidney_Cortex, Thyroid, Adipose_Subcutaneous, Muscle_Skeletal
Evidence Grading
| 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.
Limitations
- No pybedtools/pyBigWig: pure Python intervals
- Illumina-centric (450K/EPIC); uses t-test/Wilcoxon (not limma)
- No peak calling (assumes pre-called)
- API rate limits: ~20 genes per batch
Reference Files
CODE_REFERENCE.md, TOOLS_REFERENCE.md, ANALYSIS_PROCEDURES.md, QUICK_START.md
How to use tooluniverse-epigenomics on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add tooluniverse-epigenomics
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches tooluniverse-epigenomics from GitHub repository mims-harvard/tooluniverse and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate tooluniverse-epigenomics. Access the skill through slash commands (e.g., /tooluniverse-epigenomics) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★28 reviews- ★★★★★Evelyn Gupta· Dec 24, 2024
tooluniverse-epigenomics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Diego Wang· Dec 8, 2024
I recommend tooluniverse-epigenomics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Diya Lopez· Nov 27, 2024
Keeps context tight: tooluniverse-epigenomics is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Michael Jackson· Nov 15, 2024
We added tooluniverse-epigenomics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Lucas Reddy· Oct 18, 2024
Registry listing for tooluniverse-epigenomics matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Ren Rahman· Oct 6, 2024
Solid pick for teams standardizing on skills: tooluniverse-epigenomics is focused, and the summary matches what you get after install.
- ★★★★★Yash Thakker· Sep 25, 2024
Keeps context tight: tooluniverse-epigenomics is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Rahul Santra· Sep 21, 2024
Useful defaults in tooluniverse-epigenomics — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dhruvi Jain· Aug 16, 2024
Registry listing for tooluniverse-epigenomics matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Pratham Ware· Aug 12, 2024
tooluniverse-epigenomics has been reliable in day-to-day use. Documentation quality is above average for community skills.
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