---
name: kegg-database
description: "สกิลสำหรับ KEGG ฐานข้อมูล ใช้เมื่อต้องการแนวทาง ขั้นตอน Template หรือคำแนะนำเชิงปฏิบัติสำหรับงานวิเคราะห์ข้อมูล วิจัย AI ฐานข้อมูล วิทยาศาสตร์ข้อมูล และรายงานเชิงลึก ในหัวข้อนี้"
---

# KEGG ฐานข้อมูล

## Overview

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive bioinformatics resource for biological pathway analysis and molecular interaction networks.

**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. Python helper functions (`scripts/kegg_API.py`) for all KEGG REST API operations
2. Comprehensive reference documentation (`references/kegg_reference.md`) with detailed API specifications

When users request KEGG data, determine which operation is needed and use the appropriate function from `scripts/kegg_API.py`.

## Core Operations

### 1. Database Information (`kegg_info`)

Retrieve metadata and statistics about KEGG databases.

**When to use**: Understanding database structure, checking available data, getting release information.

**Usage**:
```python
from scripts.kegg_API import kegg_info

# KEGG ฐานข้อมูล
info = kegg_info('pathway')

# KEGG ฐานข้อมูล
hsa_info = kegg_info('hsa')  # Human genome
```

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

### 2. Listing Entries (`kegg_list`)

List entry identifiers and names from KEGG databases.

**When to use**: Getting all pathways for an organism, listing genes, retrieving compound catalogs.

**Usage**:
```python
from scripts.kegg_API import kegg_list

# KEGG ฐานข้อมูล
pathways = kegg_list('pathway')

# KEGG ฐานข้อมูล
hsa_pathways = kegg_list('pathway', 'hsa')

# KEGG ฐานข้อมูล
genes = kegg_list('hsa:10458+hsa:10459')
```

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

### 3. Searching (`kegg_find`)

Search KEGG databases by keywords or molecular properties.

**When to use**: Finding genes by name/description, searching compounds by formula or mass, discovering entries by keywords.

**Usage**:
```python
from scripts.kegg_API import kegg_find

# KEGG ฐานข้อมูล
results = kegg_find('genes', 'p53')
shiga_toxin = kegg_find('genes', 'shiga toxin')

# KEGG ฐานข้อมูล
compounds = kegg_find('compound', 'C7H10N4O2', 'formula')

# KEGG ฐานข้อมูล
drugs = kegg_find('drug', '300-310', 'exact_mass')
```

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

### 4. Retrieving Entries (`kegg_get`)

Get complete database entries or specific data formats.

**When to use**: Retrieving pathway details, getting gene/protein sequences, downloading pathway maps, accessing compound structures.

**Usage**:
```python
from scripts.kegg_API import kegg_get

# KEGG ฐานข้อมูล
pathway = kegg_get('hsa00010')  # Glycolysis pathway

# KEGG ฐานข้อมูล
genes = kegg_get(['hsa:10458', 'hsa:10459'])

# KEGG ฐานข้อมูล
sequence = kegg_get('hsa:10458', 'aaseq')

# KEGG ฐานข้อมูล
nt_seq = kegg_get('hsa:10458', 'ntseq')

# KEGG ฐานข้อมูล
mol_file = kegg_get('cpd:C00002', 'mol')  # ATP in MOL format

# KEGG ฐานข้อมูล
pathway_JSON = kegg_get('hsa05130', 'JSON')

# KEGG ฐานข้อมูล
pathway_img = kegg_get('hsa05130', 'image')
```

**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 (`kegg_conv`)

Convert identifiers between KEGG and external databases.

**When to use**: Integrating KEGG data with other databases, mapping gene IDs, converting compound identifiers.

**Usage**:
```python
from scripts.kegg_API import kegg_conv

# KEGG ฐานข้อมูล
conversions = kegg_conv('ncbi-geneid', 'hsa')

# KEGG ฐานข้อมูล
gene_id = kegg_conv('ncbi-geneid', 'hsa:10458')

# KEGG ฐานข้อมูล
uniprot_id = kegg_conv('uniprot', 'hsa:10458')

# KEGG ฐานข้อมูล
pubchem_ids = kegg_conv('pubchem', 'compound')

# KEGG ฐานข้อมูล
kegg_id = kegg_conv('hsa', 'ncbi-geneid')
```

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

### 6. Cross-Referencing (`kegg_link`)

Find related entries within and between KEGG databases.

**When to use**: Finding pathways containing genes, getting genes in a pathway, mapping genes to KO groups, finding compounds in pathways.

**Usage**:
```python
from scripts.kegg_API import kegg_link

# KEGG ฐานข้อมูล
pathways = kegg_link('pathway', 'hsa')

# KEGG ฐานข้อมูล
genes = kegg_link('genes', 'hsa00010')  # Glycolysis genes

# KEGG ฐานข้อมูล
gene_pathways = kegg_link('pathway', 'hsa:10458')

# KEGG ฐานข้อมูล
compounds = kegg_link('compound', 'hsa00010')

# KEGG ฐานข้อมูล
ko_groups = kegg_link('ko', 'hsa:10458')
```

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

### 7. Drug-Drug Interactions (`kegg_ddi`)

Check for drug-drug interactions.

**When to use**: Analyzing drug combinations, checking for contraindications, pharmacological research.

**Usage**:
```python
from scripts.kegg_API import kegg_ddi

# KEGG ฐานข้อมูล
interactions = kegg_ddi('D00001')

# KEGG ฐานข้อมูล
interactions = kegg_ddi(['D00001', 'D00002', 'D00003'])
```

## Common Analysis Workflows

### Workflow 1: Gene to Pathway Mapping

**Use case**: Finding pathways associated with genes of interest (e.g., for pathway enrichment analysis).

```python
from scripts.kegg_API import kegg_find, kegg_link, kegg_get

# KEGG ฐานข้อมูล
gene_results = kegg_find('genes', 'p53')

# KEGG ฐานข้อมูล
pathways = kegg_link('pathway', 'hsa:7157')  # TP53 gene

# KEGG ฐานข้อมูล
for pathway_line in pathways.split('\n'):
    if pathway_line:
        pathway_id = pathway_line.split('\t')[1].replace('path:', '')
        pathway_info = kegg_get(pathway_id)
        # Process pathway information
```

### Workflow 2: Pathway Enrichment Context

**Use case**: Getting all genes in organism pathways for enrichment analysis.

```python
from scripts.kegg_API import kegg_list, kegg_link

# KEGG ฐานข้อมูล
pathways = kegg_list('pathway', 'hsa')

# KEGG ฐานข้อมูล
for pathway_line in pathways.split('\n'):
    if pathway_line:
        pathway_id = pathway_line.split('\t')[0]
        genes = kegg_link('genes', pathway_id)
        # Process genes for enrichment analysis
```

### Workflow 3: Compound to Pathway Analysis

**Use case**: Finding metabolic pathways containing compounds of interest.

```python
from scripts.kegg_API import kegg_find, kegg_link, kegg_get

# KEGG ฐานข้อมูล
compound_results = kegg_find('compound', 'glucose')

# KEGG ฐานข้อมูล
reactions = kegg_link('reaction', 'cpd:C00031')  # Glucose

# KEGG ฐานข้อมูล
pathways = kegg_link('pathway', 'rn:R00299')  # Specific reaction

# KEGG ฐานข้อมูล
pathway_info = kegg_get('map00010')  # Glycolysis
```

### Workflow 4: Cross-Database Integration

**Use case**: Integrating KEGG data with UniProt, NCBI, or PubChem databases.

```python
from scripts.kegg_API import kegg_conv, kegg_get

# KEGG ฐานข้อมูล
uniprot_map = kegg_conv('uniprot', 'hsa')
ncbi_map = kegg_conv('ncbi-geneid', 'hsa')

# KEGG ฐานข้อมูล
for line in uniprot_map.split('\n'):
    if line:
        kegg_id, uniprot_id = line.split('\t')
        # Use external IDs for integration

# KEGG ฐานข้อมูล
sequence = kegg_get('hsa:10458', 'aaseq')
```

### Workflow 5: Organism-Specific Pathway Analysis

**Use case**: Comparing pathways across different organisms.

```python
from scripts.kegg_API import kegg_list, kegg_get

# KEGG ฐานข้อมูล
human_pathways = kegg_list('pathway', 'hsa')
mouse_pathways = kegg_list('pathway', 'mmu')
yeast_pathways = kegg_list('pathway', 'sce')

# KEGG ฐานข้อมูล
ref_pathway = kegg_get('map00010')  # Reference glycolysis

# KEGG ฐานข้อมูล
hsa_glycolysis = kegg_get('hsa00010')
mmu_glycolysis = kegg_get('mmu00010')
```

## Pathway Categories

KEGG organizes pathways into seven major categories. When interpreting pathway IDs or recommending pathways to users:

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

### Pathway IDs
- `map#####` - Reference pathway (generic, not organism-specific)
- `hsa#####` - Human pathway
- `mmu#####` - Mouse pathway

### Gene IDs
- Format: `organism:gene_number` (e.g., `hsa:10458`)

### Compound IDs
- Format: `cpd:C#####` (e.g., `cpd:C00002` for ATP)

### Drug IDs
- Format: `dr:D#####` (e.g., `dr:D00001`)

### Enzyme IDs
- Format: `ec:EC_number` (e.g., `ec:1.1.1.1`)

### KO (KEGG Orthology) IDs
- Format: `ko:K#####` (e.g., `ko:K00001`)

## API Limitations

Respect these constraints when using the KEGG API:

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

## Detailed Reference

For comprehensive API documentation, database specifications, organism codes, and advanced usage, refer to `references/kegg_reference.md`. This includes:

- Complete list of KEGG databases
- Detailed API operation syntax
- All organism codes
- HTTP status codes and error handling
- Integration with Biopython and R/Bioconductor
- Best practices for API usage

## 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

## Additional Tools

For interactive pathway visualization and annotation:
- **KEGG Mapper**: https://www.kegg.jp/kegg/mapper/
- **BlastKOALA**: Automated genome annotation
- **GhostKOALA**: Metagenome/metatranscriptome annotation
