Generate comprehensive disease research reports using 100+ ToolUniverse tools. Creates a detailed markdown report file and progressively updates it with findings from 10 research dimensions. All information includes source references. Use when users ask about diseases, syndromes, or need systematic disease analysis.
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.
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.
DO NOT show the search process to the user. Instead:
{disease_name}_research_report.mdWhen synthesizing disease etiology, trace the full pathogenic cascade:
This chain structures the Genetic & Molecular Basis (Section 3) and Biological Pathways (Section 5) sections.
| 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.
Create this file structure at the start:
# 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 |
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
# After each dimension's research:
# 1. Read current report
# 2. Replace placeholder with formatted content
# 3. Write back immediately
# 4. Continue to next dimension
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 |
After collecting data from all 10 dimensions, the report MUST answer:
When multiple databases provide different data for the same disease:
| 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 |
For a well-studied disease (e.g., Alzheimer's), the final report should include:
Total: 500+ individual data points, each with source citation.
For rare disease differential diagnosis, run: python3 skills/tooluniverse-rare-disease-diagnosis/scripts/clinical_patterns.py --type differential --symptoms 'symptom1,symptom2'