kegg-database

name: kegg_database description: Direct REST API access to KEGG (academic use only). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. Use this for direct HTTP/REST work or KEGG-specific control. license: Non-academic use of KEGG requires a commercial license metadata: skill-author: VenusFactory2.

KEGG Database

Overview

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive bioinformatics resource for biological pathway analysis and molecular interaction networks. In this project the agent exposes only download tools: save database info, entry lists, search results, entry data, ID conversions, cross-references, and drug-drug interactions to files; each returns rich JSON {status, file_info, content_preview, biological_metadata, execution_context}. For programmatic use, the package also provides query-style APIs (see Project Modules).

Important: KEGG API is made available only for academic use by academic users.

When to Use This Skill

This skill should be used when querying pathways, genes, compounds, enzymes, diseases, and drugs across multiple organisms using KEGG's REST API.

Quick Start

The skill provides:

1. Project modules in src/tools/database/kegg/: kegg_rest.py (base HTTP client), kegg_operations.py (query/download operations), kegg_api.py (backward-compat re-exports); all download functions re-exported via package. For programmatic use, import e.g. from src.tools.database.kegg import download_kegg_entry_by_id, ....
2. Reference: references/kegg_reference.md

Agent Tools (Download Only)

All return rich JSON: {status, file_info, content_preview, biological_metadata, execution_context}. Academic use only.

Project Modules (Programmatic Use)

Core Capabilities

1. Database Information

Download database info:

from src.tools.database.kegg import download_kegg_info_by_database

result = download_kegg_info_by_database("pathway", "output/kegg_info_pathway.txt")

Query in-memory:

from src.tools.database.kegg.kegg_operations import query_kegg_info_by_database

result = query_kegg_info_by_database("pathway")

Common databases: kegg, pathway, module, brite, genes, genome, compound, glycan, reaction, enzyme, disease, drug

2. Listing Entries

Download entry list:

from src.tools.database.kegg import download_kegg_list_by_database

# List all reference pathways
result = download_kegg_list_by_database("pathway", "output/kegg_pathways.txt")

# List human-specific pathways
result = download_kegg_list_by_database("pathway", "output/kegg_hsa_pathways.txt", org_or_ids="hsa")

Common organism codes: hsa (human), mmu (mouse), dme (fruit fly), sce (yeast), eco (E. coli)

3. Searching

Download search results:

from src.tools.database.kegg import download_kegg_find_by_database

# Keyword search
result = download_kegg_find_by_database("genes", "p53", "output/kegg_find_p53.txt")

# Chemical formula search (exact match)
result = download_kegg_find_by_database("compound", "C7H10N4O2", "output/kegg_find_formula.txt", option="formula")

# Molecular weight range search
result = download_kegg_find_by_database("drug", "300-310", "output/kegg_find_mass.txt", option="exact_mass")

Search options: formula (exact match), exact_mass (range), mol_weight (range)

4. Retrieving Entries

Download entry data:

from src.tools.database.kegg import download_kegg_entry_by_id

# Get pathway entry
result = download_kegg_entry_by_id("hsa00010", "output/kegg_glycolysis.txt")

# Get protein sequence (FASTA)
result = download_kegg_entry_by_id("hsa:10458", "output/kegg_gene_aaseq.fasta", format="aaseq")

# Get compound structure
result = download_kegg_entry_by_id("cpd:C00002", "output/kegg_atp.mol", format="mol")

# Get pathway as JSON (single entry only)
result = download_kegg_entry_by_id("hsa05130", "output/kegg_pathway.json", format="json")

Output formats: aaseq (protein FASTA), ntseq (nucleotide FASTA), mol (MOL format), kcf (KCF format), image (PNG), kgml (XML), json (pathway JSON)

Important: Image, KGML, and JSON formats allow only one entry at a time.

5. ID Conversion

Download ID conversion:

from src.tools.database.kegg import download_kegg_conv_by_id

# Convert KEGG gene to NCBI Gene ID
result = download_kegg_conv_by_id("ncbi-geneid", "hsa:10458", "output/kegg_conv.txt")

# Convert to UniProt
result = download_kegg_conv_by_id("uniprot", "hsa:10458", "output/kegg_conv_uniprot.txt")

Supported conversions: ncbi-geneid, ncbi-proteinid, uniprot, pubchem, chebi

6. Cross-Referencing

Download cross-references:

from src.tools.database.kegg import download_kegg_link_by_id

# Get genes in a specific pathway
result = download_kegg_link_by_id("genes", "hsa00010", "output/kegg_link_glycolysis.txt")

# Find pathways containing a specific gene
result = download_kegg_link_by_id("pathway", "hsa:10458", "output/kegg_link_gene.txt")

# Find compounds in a pathway
result = download_kegg_link_by_id("compound", "hsa00010", "output/kegg_link_compound.txt")

Common links: genes ↔ pathway, pathway ↔ compound, pathway ↔ enzyme, genes ↔ ko (orthology)

7. Drug-Drug Interactions

Download DDI data:

from src.tools.database.kegg import download_kegg_ddi_by_id

result = download_kegg_ddi_by_id("D00001", "output/kegg_ddi.txt")

Common Workflows

Workflow 1: Gene to Pathway Mapping (download)

from src.tools.database.kegg import (
    download_kegg_find_by_database,
    download_kegg_link_by_id,
    download_kegg_entry_by_id,
)

# Step 1: Find gene by name
download_kegg_find_by_database("genes", "p53", "output/kegg_p53_genes.txt")

# Step 2: Link gene to pathways
download_kegg_link_by_id("pathway", "hsa:7157", "output/kegg_p53_pathways.txt")

# Step 3: Get pathway details
download_kegg_entry_by_id("hsa05200", "output/kegg_cancer_pathway.txt")

Workflow 2: Compound to Pathway Analysis (download)

from src.tools.database.kegg import (
    download_kegg_find_by_database,
    download_kegg_link_by_id,
    download_kegg_entry_by_id,
)

# Step 1: Search for compound
download_kegg_find_by_database("compound", "glucose", "output/kegg_glucose.txt")

# Step 2: Link compound to reactions/pathways
download_kegg_link_by_id("reaction", "cpd:C00031", "output/kegg_glucose_reactions.txt")
download_kegg_link_by_id("pathway", "rn:R00299", "output/kegg_reaction_pathways.txt")

# Step 3: Get pathway details
download_kegg_entry_by_id("map00010", "output/kegg_glycolysis.txt")

Workflow 3: Cross-Database Integration (download)

from src.tools.database.kegg import (
    download_kegg_conv_by_id,
    download_kegg_entry_by_id,
)

# Step 1: Convert KEGG gene IDs to external database IDs
download_kegg_conv_by_id("uniprot", "hsa:10458", "output/kegg_to_uniprot.txt")
download_kegg_conv_by_id("ncbi-geneid", "hsa:10458", "output/kegg_to_ncbi.txt")

# Step 2: Get sequences using KEGG
download_kegg_entry_by_id("hsa:10458", "output/kegg_gene_seq.fasta", format="aaseq")

Workflow 4: Organism-Specific Pathway Analysis (programmatic)

from src.tools.database.kegg.kegg_operations import (
    query_kegg_list_by_database,
    query_kegg_entry_by_id,
)

# List pathways for multiple organisms
human_pathways = query_kegg_list_by_database("pathway", "hsa")
mouse_pathways = query_kegg_list_by_database("pathway", "mmu")

# Get organism-specific pathway details
hsa_glycolysis = query_kegg_entry_by_id("hsa00010")
mmu_glycolysis = query_kegg_entry_by_id("mmu00010")

Response Format

Download Response (success)

{
  "status": "success",
  "file_info": {
    "file_path": "/absolute/path/to/file.txt",
    "file_name": "file.txt",
    "file_size": 12345,
    "format": "txt"
  },
  "content_preview": "first 500 chars...",
  "biological_metadata": {"database": "pathway"},
  "execution_context": {"download_time_ms": 234, "source": "KEGG"}
}

Query Response (success)

{
  "status": "success",
  "content": "{...full JSON...}",
  "content_preview": "first 500 chars...",
  "biological_metadata": {"database": "pathway"},
  "execution_context": {"query_time_ms": 123, "source": "KEGG"}
}

Error Response

{
  "status": "error",
  "error": {"type": "QueryError", "message": "...", "suggestion": "..."},
  "file_info": null
}

Pathway Categories

KEGG organizes pathways into seven major categories:

1. Metabolism (e.g., map00010 - Glycolysis, map00190 - Oxidative phosphorylation)
2. Genetic Information Processing (e.g., map03010 - Ribosome, map03040 - Spliceosome)
3. Environmental Information Processing (e.g., map04010 - MAPK signaling, map02010 - ABC transporters)
4. Cellular Processes (e.g., map04140 - Autophagy, map04210 - Apoptosis)
5. Organismal Systems (e.g., map04610 - Complement cascade, map04910 - Insulin signaling)
6. Human Diseases (e.g., map05200 - Pathways in cancer, map05010 - Alzheimer disease)
7. Drug Development (chronological and target-based classifications)

Reference references/kegg_reference.md for detailed pathway lists and classifications.

Important Identifiers and Formats

Helper Scripts

Scripts live in src/tools/database/kegg/. Import from package: from src.tools.database.kegg import ...

kegg_operations.py

Central operations module providing both query and download functions:

• query_kegg_info_by_database(database) — returns rich JSON in memory
• query_kegg_list_by_database(database, org_or_ids) — returns rich JSON in memory
• query_kegg_find_by_database(database, query, option) — returns rich JSON in memory
• query_kegg_entry_by_id(entry_id, format) — returns rich JSON in memory
• query_kegg_conv_by_id(target_db, source_id) — returns rich JSON in memory
• query_kegg_link_by_id(target_db, source_id) — returns rich JSON in memory
• query_kegg_ddi_by_id(drug_id) — returns rich JSON in memory
• download_kegg_info_by_database(database, out_path) — save to file, return rich JSON
• download_kegg_list_by_database(database, out_path, org_or_ids) — save to file, return rich JSON
• download_kegg_find_by_database(database, query, out_path, option) — save to file, return rich JSON
• download_kegg_entry_by_id(entry_id, out_path, format) — save to file, return rich JSON
• download_kegg_conv_by_id(target_db, source_id, out_path) — save to file, return rich JSON
• download_kegg_link_by_id(target_db, source_id, out_path) — save to file, return rich JSON
• download_kegg_ddi_by_id(drug_id, out_path) — save to file, return rich JSON

Backward-compat aliases: kegg_info, kegg_list, kegg_find, kegg_get, kegg_conv, kegg_link, kegg_ddi.

Test: bash script/tools/database/test_kegg.sh — runs kegg_operations.py --test, outputs under example/database/kegg/.

kegg_rest.py

Base HTTP client:

• kegg_request(operation, *path_parts) — GET request to https://rest.kegg.jp/, returns response text
• _join_ids(entry_id) — format one or multiple entry IDs for URL (max 10, + separated)

kegg_api.py

Backward-compat entry point: re-exports all query/download functions and legacy aliases.

API Limitations

1. Entry limits: Maximum 10 entries per operation (except image/kgml/json: 1 entry only)
2. Academic use: API is for academic use only; commercial use requires licensing
3. HTTP status codes: Check for 200 (success), 400 (bad request), 404 (not found)
4. Rate limiting: No explicit limit, but avoid rapid-fire requests

Troubleshooting

404 Not Found: Entry or database doesn't exist; verify IDs and organism codes 400 Bad Request: Syntax error in API call; check parameter formatting Empty results: Search term may not match entries; try broader keywords Image/KGML errors: These formats only work with single entries; remove batch processing

Resources

references/kegg_reference.md

Comprehensive API documentation including complete database list, operation syntax, all organism codes, HTTP status codes, and integration with Biopython/R.

External

• KEGG website: https://www.kegg.jp/
• KEGG Mapper: https://www.kegg.jp/kegg/mapper/
• BlastKOALA: Automated genome annotation
• GhostKOALA: Metagenome/metatranscriptome annotation