tooluniverse-disease-research

What this skill does

# ToolUniverse Disease Research

Generate a comprehensive disease research report with full source citations. The report is created as a markdown file and progressively updated during research.

**IMPORTANT**: Always use English disease names and search terms in tool calls. Respond in the user's language.

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## LOOK UP, DON'T GUESS

When asked about a disease, query Orphanet/OMIM/DisGeNET FIRST. Don't rely on memory for prevalence, genetics, or treatment — these change over time. When you're not sure about a fact, your first instinct should be to SEARCH for it using tools, not to reason harder from memory.

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## When to Use

- User asks about any disease, syndrome, or medical condition
- Needs comprehensive disease intelligence or a detailed research report
- Asks "what do we know about [disease]?"

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## Core Workflow: Report-First Approach

**DO NOT** show the search process to the user. Instead:

1. **Create report file first** - Initialize `{disease_name}_research_report.md`
2. **Research each dimension** - Use all relevant tools
3. **Update report progressively** - Write findings after each dimension
4. **Include citations** - Every fact must reference its source tool

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## Disease Mechanism Reasoning

When synthesizing disease etiology, trace the full pathogenic cascade:
1. **Genetic basis** - Which variants (rare or common) confer risk, and in which genes?
2. **Molecular mechanism** - How do those variants alter protein function, expression, or regulation?
3. **Cellular effect** - What downstream cellular processes are disrupted (signaling, metabolism, stress response)?
4. **Tissue/organ manifestation** - How does cellular dysfunction present as organ-level pathology?

This chain structures the Genetic & Molecular Basis (Section 3) and Biological Pathways (Section 5) sections.

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## 10 Research Dimensions

| Dim | Section | Key Tools |
|-----|---------|-----------|
| 1 | Identity & Classification | OSL_get_efo_id_by_disease_name, ols_search_efo_terms, ols_get_efo_term, umls_search_concepts, icd_search_codes, snomed_search_concepts |
| 2 | Clinical Presentation | OpenTargets phenotypes, HPO lookup, MedlinePlus |
| 3 | Genetic & Molecular Basis | OpenTargets targets, ClinVar variants, GWAS associations, gnomAD |
| 4 | Treatment Landscape | OpenTargets drugs, clinical trials, GtoPdb |
| 5 | Biological Pathways | Reactome pathways, humanbase_ppi_analysis, GTEx expression, HPA |
| 6 | Epidemiology & Literature | PubMed, OpenAlex, Europe PMC, Semantic Scholar |
| 7 | Similar Diseases | OpenTargets similar entities |
| 8 | Cancer-Specific (if applicable) | CIViC genes/variants/therapies |
| 9 | Pharmacology | GtoPdb targets/interactions/ligands |
| 10 | Drug Safety | OpenTargets warnings, clinical trial AEs, FAERS |

See: tool_usage_details.md for complete tool calls per section.

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## Report Template

Create this file structure at the start:

```markdown
# Disease Research Report: {Disease Name}

**Report Generated**: {date}
**Disease Identifiers**: (to be filled)

---

## Executive Summary
(Brief 3-5 sentence overview - fill after all research complete)

---

## 1. Disease Identity & Classification
### Ontology Identifiers
| System | ID | Source |

### Synonyms & Alternative Names
### Disease Hierarchy

---

## 2. Clinical Presentation
### Phenotypes (HPO)
| HPO ID | Phenotype | Description | Source |

### Symptoms & Signs
### Diagnostic Criteria

---

## 3. Genetic & Molecular Basis
### Associated Genes
| Gene | Score | Ensembl ID | Evidence | Source |

### GWAS Associations
| SNP | P-value | Odds Ratio | Study | Source |

### Pathogenic Variants (ClinVar)

---

## 4. Treatment Landscape
### Approved Drugs
| Drug | ChEMBL ID | Mechanism | Phase | Target | Source |

### Clinical Trials
| NCT ID | Title | Phase | Status | Source |

---

## 5. Biological Pathways & Mechanisms

## 6. Epidemiology & Risk Factors

## 7. Literature & Research Activity

## 8. Similar Diseases & Comorbidities

## 9. Cancer-Specific Information (if applicable)

## 10. Drug Safety & Adverse Events

---

## References
### Tools Used
| # | Tool | Parameters | Section | Items Retrieved |
```

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## Citation Format

Every piece of data MUST include its source:

**In tables**: Add a `Source` column with tool name
**In lists**: `- Finding [Source: tool_name]`
**In prose**: `(Source: tool_name, query: "...")`
**References section**: Complete tool usage log with parameters

---

## Progressive Update Pattern

```python
# After each dimension's research:
# 1. Read current report
# 2. Replace placeholder with formatted content
# 3. Write back immediately
# 4. Continue to next dimension
```

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## Evidence Grading & Interpretation

Every finding in the report should be graded:

| Grade | Criteria | Example |
|-------|---------|---------|
| **T1 (Strong)** | Replicated genetic evidence (GWAS, rare variants), FDA-approved therapy | BRCA1 → breast cancer; trastuzumab for HER2+ |
| **T2 (Moderate)** | Single genetic study, phase II+ trial data, strong biological evidence | FOXO3 → longevity (centenarian studies) |
| **T3 (Association)** | Observational data, gene expression changes, pathway membership | IL-6 elevated in Alzheimer's CSF |
| **T4 (Computational)** | Network proximity, text mining, predicted associations | DisGeNET text-mined gene-disease link |

### Synthesis Questions (answer in Executive Summary)

After collecting data from all 10 dimensions, the report MUST answer:

1. **What causes this disease?** Summarize the genetic architecture (monogenic vs polygenic, key loci, penetrance)
2. **What are the therapeutic options?** Ranked by evidence level and approval status
3. **What biomarkers exist?** For diagnosis, prognosis, and treatment selection
4. **What's the unmet need?** What aspects lack effective treatment or understanding?
5. **What are the active research frontiers?** Based on clinical trials and recent publications

### Interpreting Cross-Database Concordance

When multiple databases provide different data for the same disease:
- **OpenTargets + DisGeNET + OMIM agree on a gene**: T1 evidence — high confidence
- **Only OpenTargets reports an association**: Check the datasource scores — genetic_association > literature > animal_model
- **DisGeNET score > 0.5 but not in OpenTargets**: May be text-mined; verify with PubMed
- **Gene in GWAS but not OMIM**: Likely a complex disease susceptibility locus, not Mendelian

### Handling Conflicting Data

| Conflict | Resolution |
|----------|-----------|
| Different prevalence estimates across sources | Report range; note the most recent/largest study |
| Drug approved in one country but not another | Note regulatory status per region |
| Gene-disease association in one DB but absent in another | Grade by evidence type; text-mining alone is T4 |
| Clinical trial results contradict label indications | The trial result is newer evidence; note both |

---

## Final Report Quality Checklist

- [ ] All 10 sections have content (or marked "No data available")
- [ ] Every data point has a source citation
- [ ] Executive summary reflects key findings
- [ ] References section lists all tools used
- [ ] Tables properly formatted
- [ ] No placeholder text remains

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## Expected Output Scale

For a well-studied disease (e.g., Alzheimer's), the final report should include:
- 5+ ontology IDs, 10+ synonyms, disease hierarchy
- 20+ phenotypes with HPO IDs
- 50+ genes, 30+ GWAS associations, 100+ ClinVar variants
- 20+ drugs, 50+ clinical trials
- 10+ pathways, PPI network, expression data
- 100+ publications
- 15+ similar diseases
- Drug warnings and adverse events

Total: 500+ individual data points, each with source citation.

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## Cross-Skill References

For rare disease differential diagnosis, run: `python3 skills/tooluniverse-rare-disease-diagnosis/scripts/clinical_patterns.py --type differential --symptoms 'symptom1,symptom2'`

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## Reference Files

- **[REPORT_TEMPLATE.md](REPORT_TEMPLATE.md)** - Full rep