Generate comprehensive disease research reports covering genetics (causal genes, GWAS, OMIM), pathways (Reactome, KEGG), drugs (existing therapies, repurposing candidates), clinical trials, epidemiology (prevalence, incidence), and phenotypes (HPO). Use for full disease overviews, comprehensive disease characterization, and orphan/rare-disease profiling.
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.
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.
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]?"
Core Workflow: Report-First Approach
DO NOT show the search process to the user. Instead:
Create report file first - Initialize {disease_name}_research_report.md
Research each dimension - Use all relevant tools
Update report progressively - Write findings after each dimension
Include citations - Every fact must reference its source tool
Disease Mechanism Reasoning
When synthesizing disease etiology, trace the full pathogenic cascade:
Genetic basis - Which variants (rare or common) confer risk, and in which genes?
Molecular mechanism - How do those variants alter protein function, expression, or regulation?
Cellular effect - What downstream cellular processes are disrupted (signaling, metabolism, stress response)?
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.
See: tool_usage_details.md for complete tool calls per section.
Normalizing free text to ontology IDs (Dimension 1)
When the input is messy free text (a sample attribute, a synonym, a tissue/organism label) rather than a clean disease name, use ZOOMA_annotate_text to map it to standardized ontology terms (EFO/MONDO/UBERON/etc.) before lookup. It returns each match as an ontology IRI with a confidence rating (HIGH/GOOD/MEDIUM/LOW), so you can keep only high-confidence hits and feed the resolved ID into OLS / OpenTargets.
tu.run_tool("ZOOMA_annotate_text", {
"property_value": "asthma", # free text to resolve"property_type": "disease", # optional context hint"min_confidence": "HIGH", # drop fuzzy matches"max_results": 3,
})
# -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"],# "curies": ["MONDO:0004979"], "confidence": "HIGH", "source": "zooma", ...}]# Restrict to one ontology source (e.g. EFO) when you need a specific namespace:
tu.run_tool("ZOOMA_annotate_text", {"property_value": "diabetes", "ontologies": "efo"})
# Inspect which curated datasources back ZOOMA annotations (for provenance):
tu.run_tool("ZOOMA_list_datasources", {})
# -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...]
Each match also carries a ready-to-use curies field (e.g. MONDO:0004979) so you can feed the resolved ID straight into OLS / OpenTargets without parsing the IRI. ZOOMA is the live replacement for the retired OxO cross-reference service; pair it with ols_get_efo_term to expand the resolved IRI into labels, synonyms, and hierarchy.
Report Template
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 |
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
# After each dimension's research:# 1. Read current report# 2. Replace placeholder with formatted content# 3. Write back immediately# 4. Continue to next dimension
