| name | gene-list-agent |
| description | Functional analysis of a gene list — batch summaries, pathway mapping, protein interactions, tissue expression, and phenotype associations |
Perform a functional analysis of the gene list: $ARGUMENTS
Use the MCP tools to characterize this gene set — summarize each gene, find shared pathways, map interactions, assess tissue expression patterns, and identify phenotype associations. Follow the steps below. If a step fails for a specific gene, note the gap and continue.
Input Parsing
Extract gene symbols from the input. The user may provide:
- A comma-separated list:
TP53, BRCA1, EGFR, PTEN
- A space-separated list:
TP53 BRCA1 EGFR PTEN
- A description with embedded genes — extract the gene symbols
Normalize all symbols to uppercase. If more than 20 genes are provided, work with the first 20 and note the truncation.
Data Gathering Steps
1. Batch Gene Summary
- Call
batch_gene_summary with all gene symbols (comma-separated) and taxon human.
- Extract: full name, description, chromosome location, gene type for each gene.
- If batch_gene_summary fails, fall back to individual
datasets_summary_gene calls for each gene.
2. Pathway Analysis
For each gene (up to 10):
- Call
kegg_get_pathway with the gene symbol to find associated pathways.
- Collect all pathway IDs and names.
- After all genes are queried, identify shared pathways — pathways that appear for 2+ genes in the list.
- Rank shared pathways by the number of input genes they contain.
3. Protein Interaction Network
- Call
string_get_interactions with all gene symbols joined by commas (species: 9606, required_score: 700, limit: 10).
- This shows interactions between the input genes and with other proteins.
- Identify which input genes interact directly with each other.
4. Tissue Expression Patterns
For each gene (up to 10):
- Call
gtex_get_expression with the gene symbol.
- Record the top 5 tissues by expression level for each gene.
- After all genes are queried, identify shared high-expression tissues — tissues that appear in the top 5 for multiple genes.
5. Functional Annotation
For each gene (up to 5 — focus on the most interesting or least familiar):
- Call
uniprot_search with the gene symbol and organism_id:9606 (reviewed: true).
- Call
uniprot_get_protein with the accession to get GO terms and function descriptions.
- Collect all GO Biological Process terms across the gene set.
- Identify recurring GO terms (appearing for 2+ genes) as enriched biological themes.
6. Clinical Relevance
- Call
clinvar_search with each gene symbol to check for pathogenic variants.
- Count pathogenic/likely pathogenic variants per gene.
- Note which genes have the most clinical variant data.
7. Phenotype Associations
For each gene (up to 10):
- Call
hpo_search with the gene symbol (category: search) to find associated phenotypes.
- Collect phenotype terms across the set and identify recurring phenotypes.
Report Format
# Gene List Functional Analysis
## Input
- Number of genes analyzed: N
- Gene symbols: [list]
## Gene Summary Table
| Gene | Full Name | Chr | Type | ClinVar Pathogenic Variants |
|------|-----------|-----|------|-----------------------------|
| TP53 | Tumor protein p53 | 17 | protein-coding | 1,247 |
| ... | ... | ... | ... | ... |
## Shared Pathway Analysis
### Top Shared Pathways
Pathways containing 2+ genes from the input list:
| Pathway | KEGG ID | Genes in List | Total Genes in Pathway |
|---------|---------|---------------|----------------------|
| p53 signaling | hsa04115 | TP53, BRCA1, PTEN | — |
| ... | ... | ... | ... |
### Per-Gene Pathway Summary
Brief note on unique pathways for each gene not shared with others.
## Protein Interaction Network
- Direct interactions between input genes (from STRING)
- Key hub genes (genes with most connections)
- Notable interaction partners outside the input list
## Tissue Expression Patterns
- Shared high-expression tissues across the gene set
- Tissue expression summary table:
| Tissue | Genes Highly Expressed | Notable |
|--------|----------------------|---------|
## Functional Themes
- Recurring GO Biological Process terms (enriched themes)
- Recurring GO Molecular Function terms
- Common subcellular localizations
## Phenotype Associations
- Recurring HPO phenotype terms across the gene set
- Disease associations shared by multiple genes
## Clinical Summary
- Genes with highest ClinVar pathogenic variant burden
- Shared disease associations
## Interpretation
Brief synthesis:
- What biological theme(s) unite this gene set?
- Are they co-expressed in specific tissues?
- Do they share pathway memberships or interact directly?
- What clinical significance does this gene set have collectively?
## Data Sources
List which databases were queried and their success/failure status.
Keep the report factual — only include data returned by the tools. For pathway and enrichment analysis, clearly state that you are reporting observed overlaps, not formal statistical enrichment (no p-values). If a gene returns no data from a source, note it in the relevant section.
