KEGG & Reactome

Map genes and metabolites to biological pathways using KEGG and Reactome. Run enrichment analysis on gene or metabolite sets and download pathway diagrams.

TOOLkegg_enrichment

Run KEGG pathway enrichment on a gene list. Returns enriched pathways with adjusted p-values, gene overlap counts, and optionally saves a colour-mapped pathway image.

Parameter	Type	Description
gene_list *	string[]	HGNC gene symbols or Entrez IDs.
species	string	KEGG organism code: `hsa` (human), `mmu` (mouse) (default: `hsa`).
pvalue_threshold	number	Adjusted p-value cutoff (default 0.05).
workspace_id	string	Set to save results and pathway diagrams to workspace.

from smartsbio import SmartsBio

client = SmartsBio(api_key="sk_live_...")
ws_id = client.workspaces.list()[0].id

gene_list = ["BRCA1", "BRCA2", "ATM", "CHEK2", "RAD51", "PALB2", "TP53", "CDK2", "CCND1"]

result = client.tools.run(
    tool_id="kegg_enrichment",
    input={
        "gene_list": gene_list,
        "species": "hsa",
        "pvalue_threshold": 0.05,
        "workspace_id": ws_id,
        "output_path": "pathways/kegg/",
    },
)
print(f"Enriched pathways: {len(result['pathways'])}")
for p in result["pathways"][:5]:
    print(f"  {p['id']:<10} {p['name']:<45} p={p['padj']:.2e}  k={p['k']}/{p['n']}")

# Download pathway diagram for top hit
diagram = client.files.download(result["pathways"][0]["diagram_path"], workspace_id=ws_id, dest="./pathways/")
print(f"Pathway diagram saved to {diagram}")

TOOLreactome_enrichment

Run pathway enrichment against the Reactome hierarchy. Returns parent–child pathway context and gene-level overlap details.

from smartsbio import SmartsBio

client = SmartsBio(api_key="sk_live_...")
ws_id = client.workspaces.list()[0].id

gene_list = ["EGFR", "ERBB2", "PIK3CA", "AKT1", "MTOR", "RPS6KB1", "PTEN"]

result = client.tools.run(
    tool_id="reactome_enrichment",
    input={
        "gene_list": gene_list,
        "species": "Homo sapiens",
        "pvalue_threshold": 0.05,
        "workspace_id": ws_id,
        "output_path": "pathways/reactome/",
    },
)
for p in result["pathways"][:5]:
    print(f"  {p['pathway_id']:<15} {p['name'][:50]:<50} FDR={p['fdr']:.2e}")

# Download full results table
csv = client.files.download(result["output_path"], workspace_id=ws_id, dest="./pathways/")
print(f"Results saved to {csv}")

Download a KEGG pathway diagram

Retrieve a colour-highlighted pathway image for a specific KEGG pathway ID.

from smartsbio import SmartsBio

client = SmartsBio(api_key="sk_live_...")
ws_id = client.workspaces.list()[0].id

result = client.tools.run(
    tool_id="kegg_pathway_diagram",
    input={
        "pathway_id": "hsa05200",   # Pathways in cancer
        "highlight_genes": ["TP53", "KRAS", "EGFR", "MYC"],
        "workspace_id": ws_id,
        "output_path": "diagrams/",
    },
)
diagram = client.files.download(result["diagram_path"], workspace_id=ws_id, dest="./diagrams/")
print(f"Pathway image saved to {diagram}")