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Python helper functions (`scripts\u002Fkegg_api.py`) for all KEGG REST API operations\n2. Comprehensive reference documentation (`references\u002Fkegg_reference.md`) with detailed API specifications\n\nWhen users request KEGG data, determine which operation is needed and use the appropriate function from `scripts\u002Fkegg_api.py`.\n\n## Core Operations\n\n### 1. Database Information (`kegg_info`)\n\nRetrieve metadata and statistics about KEGG databases.\n\n**When to use**: Understanding database structure, checking available data, getting release information.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_info\n\n# Get pathway database info\ninfo = kegg_info('pathway')\n\n# Get organism-specific info\nhsa_info = kegg_info('hsa')  # Human genome\n```\n\n**Common databases**: `kegg`, `pathway`, `module`, `brite`, `genes`, `genome`, `compound`, `glycan`, `reaction`, `enzyme`, `disease`, `drug`\n\n### 2. Listing Entries (`kegg_list`)\n\nList entry identifiers and names from KEGG databases.\n\n**When to use**: Getting all pathways for an organism, listing genes, retrieving compound catalogs.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_list\n\n# List all reference pathways\npathways = kegg_list('pathway')\n\n# List human-specific pathways\nhsa_pathways = kegg_list('pathway', 'hsa')\n\n# List specific genes (max 10)\ngenes = kegg_list('hsa:10458+hsa:10459')\n```\n\n**Common organism codes**: `hsa` (human), `mmu` (mouse), `dme` (fruit fly), `sce` (yeast), `eco` (E. coli)\n\n### 3. Searching (`kegg_find`)\n\nSearch KEGG databases by keywords or molecular properties.\n\n**When to use**: Finding genes by name\u002Fdescription, searching compounds by formula or mass, discovering entries by keywords.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_find\n\n# Keyword search\nresults = kegg_find('genes', 'p53')\nshiga_toxin = kegg_find('genes', 'shiga toxin')\n\n# Chemical formula search (exact match)\ncompounds = kegg_find('compound', 'C7H10N4O2', 'formula')\n\n# Molecular weight range search\ndrugs = kegg_find('drug', '300-310', 'exact_mass')\n```\n\n**Search options**: `formula` (exact match), `exact_mass` (range), `mol_weight` (range)\n\n### 4. Retrieving Entries (`kegg_get`)\n\nGet complete database entries or specific data formats.\n\n**When to use**: Retrieving pathway details, getting gene\u002Fprotein sequences, downloading pathway maps, accessing compound structures.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_get\n\n# Get pathway entry\npathway = kegg_get('hsa00010')  # Glycolysis pathway\n\n# Get multiple entries (max 10)\ngenes = kegg_get(['hsa:10458', 'hsa:10459'])\n\n# Get protein sequence (FASTA)\nsequence = kegg_get('hsa:10458', 'aaseq')\n\n# Get nucleotide sequence\nnt_seq = kegg_get('hsa:10458', 'ntseq')\n\n# Get compound structure\nmol_file = kegg_get('cpd:C00002', 'mol')  # ATP in MOL format\n\n# Get pathway as JSON (single entry only)\npathway_json = kegg_get('hsa05130', 'json')\n\n# Get pathway image (single entry only)\npathway_img = kegg_get('hsa05130', 'image')\n```\n\n**Output formats**: `aaseq` (protein FASTA), `ntseq` (nucleotide FASTA), `mol` (MOL format), `kcf` (KCF format), `image` (PNG), `kgml` (XML), `json` (pathway JSON)\n\n**Important**: Image, KGML, and JSON formats allow only one entry at a time.\n\n### 5. ID Conversion (`kegg_conv`)\n\nConvert identifiers between KEGG and external databases.\n\n**When to use**: Integrating KEGG data with other databases, mapping gene IDs, converting compound identifiers.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_conv\n\n# Convert all human genes to NCBI Gene IDs\nconversions = kegg_conv('ncbi-geneid', 'hsa')\n\n# Convert specific gene\ngene_id = kegg_conv('ncbi-geneid', 'hsa:10458')\n\n# Convert to UniProt\nuniprot_id = kegg_conv('uniprot', 'hsa:10458')\n\n# Convert compounds to PubChem\npubchem_ids = kegg_conv('pubchem', 'compound')\n\n# Reverse conversion (NCBI Gene ID to KEGG)\nkegg_id = kegg_conv('hsa', 'ncbi-geneid')\n```\n\n**Supported conversions**: `ncbi-geneid`, `ncbi-proteinid`, `uniprot`, `pubchem`, `chebi`\n\n### 6. Cross-Referencing (`kegg_link`)\n\nFind related entries within and between KEGG databases.\n\n**When to use**: Finding pathways containing genes, getting genes in a pathway, mapping genes to KO groups, finding compounds in pathways.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_link\n\n# Find pathways linked to human genes\npathways = kegg_link('pathway', 'hsa')\n\n# Get genes in a specific pathway\ngenes = kegg_link('genes', 'hsa00010')  # Glycolysis genes\n\n# Find pathways containing a specific gene\ngene_pathways = kegg_link('pathway', 'hsa:10458')\n\n# Find compounds in a pathway\ncompounds = kegg_link('compound', 'hsa00010')\n\n# Map genes to KO (orthology) groups\nko_groups = kegg_link('ko', 'hsa:10458')\n```\n\n**Common links**: genes ↔ pathway, pathway ↔ compound, pathway ↔ enzyme, genes ↔ ko (orthology)\n\n### 7. Drug-Drug Interactions (`kegg_ddi`)\n\nCheck for drug-drug interactions.\n\n**When to use**: Analyzing drug combinations, checking for contraindications, pharmacological research.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_ddi\n\n# Check single drug\ninteractions = kegg_ddi('D00001')\n\n# Check multiple drugs (max 10)\ninteractions = kegg_ddi(['D00001', 'D00002', 'D00003'])\n```\n\n## Common Analysis Workflows\n\n### Workflow 1: Gene to Pathway Mapping\n\n**Use case**: Finding pathways associated with genes of interest (e.g., for pathway enrichment analysis).\n\n```python\nfrom scripts.kegg_api import kegg_find, kegg_link, kegg_get\n\n# Step 1: Find gene ID by name\ngene_results = kegg_find('genes', 'p53')\n\n# Step 2: Link gene to pathways\npathways = kegg_link('pathway', 'hsa:7157')  # TP53 gene\n\n# Step 3: Get detailed pathway information\nfor pathway_line in pathways.split('\\n'):\n    if pathway_line:\n        pathway_id = pathway_line.split('\\t')[1].replace('path:', '')\n        pathway_info = kegg_get(pathway_id)\n        # Process pathway information\n```\n\n### Workflow 2: Pathway Enrichment Context\n\n**Use case**: Getting all genes in organism pathways for enrichment analysis.\n\n```python\nfrom scripts.kegg_api import kegg_list, kegg_link\n\n# Step 1: List all human pathways\npathways = kegg_list('pathway', 'hsa')\n\n# Step 2: For each pathway, get associated genes\nfor pathway_line in pathways.split('\\n'):\n    if pathway_line:\n        pathway_id = pathway_line.split('\\t')[0]\n        genes = kegg_link('genes', pathway_id)\n        # Process genes for enrichment analysis\n```\n\n### Workflow 3: Compound to Pathway Analysis\n\n**Use case**: Finding metabolic pathways containing compounds of interest.\n\n```python\nfrom scripts.kegg_api import kegg_find, kegg_link, kegg_get\n\n# Step 1: Search for compound\ncompound_results = kegg_find('compound', 'glucose')\n\n# Step 2: Link compound to reactions\nreactions = kegg_link('reaction', 'cpd:C00031')  # Glucose\n\n# Step 3: Link reactions to pathways\npathways = kegg_link('pathway', 'rn:R00299')  # Specific reaction\n\n# Step 4: Get pathway details\npathway_info = kegg_get('map00010')  # Glycolysis\n```\n\n### Workflow 4: Cross-Database Integration\n\n**Use case**: Integrating KEGG data with UniProt, NCBI, or PubChem databases.\n\n```python\nfrom scripts.kegg_api import kegg_conv, kegg_get\n\n# Step 1: Convert KEGG gene IDs to external database IDs\nuniprot_map = kegg_conv('uniprot', 'hsa')\nncbi_map = kegg_conv('ncbi-geneid', 'hsa')\n\n# Step 2: Parse conversion results\nfor line in uniprot_map.split('\\n'):\n    if line:\n        kegg_id, uniprot_id = line.split('\\t')\n        # Use external IDs for integration\n\n# Step 3: Get sequences using KEGG\nsequence = kegg_get('hsa:10458', 'aaseq')\n```\n\n### Workflow 5: Organism-Specific Pathway Analysis\n\n**Use case**: Comparing pathways across different organisms.\n\n```python\nfrom scripts.kegg_api import kegg_list, kegg_get\n\n# Step 1: List pathways for multiple organisms\nhuman_pathways = kegg_list('pathway', 'hsa')\nmouse_pathways = kegg_list('pathway', 'mmu')\nyeast_pathways = kegg_list('pathway', 'sce')\n\n# Step 2: Get reference pathway for comparison\nref_pathway = kegg_get('map00010')  # Reference glycolysis\n\n# Step 3: Get organism-specific versions\nhsa_glycolysis = kegg_get('hsa00010')\nmmu_glycolysis = kegg_get('mmu00010')\n```\n\n## Pathway Categories\n\nKEGG organizes pathways into seven major categories. When interpreting pathway IDs or recommending pathways to users:\n\n1. **Metabolism** (e.g., `map00010` - Glycolysis, `map00190` - Oxidative phosphorylation)\n2. **Genetic Information Processing** (e.g., `map03010` - Ribosome, `map03040` - Spliceosome)\n3. **Environmental Information Processing** (e.g., `map04010` - MAPK signaling, `map02010` - ABC transporters)\n4. **Cellular Processes** (e.g., `map04140` - Autophagy, `map04210` - Apoptosis)\n5. **Organismal Systems** (e.g., `map04610` - Complement cascade, `map04910` - Insulin signaling)\n6. **Human Diseases** (e.g., `map05200` - Pathways in cancer, `map05010` - Alzheimer disease)\n7. **Drug Development** (chronological and target-based classifications)\n\nReference `references\u002Fkegg_reference.md` for detailed pathway lists and classifications.\n\n## Important Identifiers and Formats\n\n### Pathway IDs\n- `map#####` - Reference pathway (generic, not organism-specific)\n- `hsa#####` - Human pathway\n- `mmu#####` - Mouse pathway\n\n### Gene IDs\n- Format: `organism:gene_number` (e.g., `hsa:10458`)\n\n### Compound IDs\n- Format: `cpd:C#####` (e.g., `cpd:C00002` for ATP)\n\n### Drug IDs\n- Format: `dr:D#####` (e.g., `dr:D00001`)\n\n### Enzyme IDs\n- Format: `ec:EC_number` (e.g., `ec:1.1.1.1`)\n\n### KO (KEGG Orthology) IDs\n- Format: `ko:K#####` (e.g., `ko:K00001`)\n\n## API Limitations\n\nRespect these constraints when using the KEGG API:\n\n1. **Entry limits**: Maximum 10 entries per operation (except image\u002Fkgml\u002Fjson: 1 entry only)\n2. **Academic use**: API is for academic use only; commercial use requires licensing\n3. **HTTP status codes**: Check for 200 (success), 400 (bad request), 404 (not found)\n4. **Rate limiting**: No explicit limit, but avoid rapid-fire requests\n\n## Detailed Reference\n\nFor comprehensive API documentation, database specifications, organism codes, and advanced usage, refer to `references\u002Fkegg_reference.md`. This includes:\n\n- Complete list of KEGG databases\n- Detailed API operation syntax\n- All organism codes\n- HTTP status codes and error handling\n- Integration with Biopython and R\u002FBioconductor\n- Best practices for API usage\n\n## Troubleshooting\n\n**404 Not Found**: Entry or database doesn't exist; verify IDs and organism codes\n**400 Bad Request**: Syntax error in API call; check parameter formatting\n**Empty results**: Search term may not match entries; try broader keywords\n**Image\u002FKGML errors**: These formats only work with single entries; remove batch processing\n\n## Additional Tools\n\nFor interactive pathway visualization and annotation:\n- **KEGG Mapper**: https:\u002F\u002Fwww.kegg.jp\u002Fkegg\u002Fmapper\u002F\n- **BlastKOALA**: Automated genome annotation\n- **GhostKOALA**: Metagenome\u002Fmetatranscriptome 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