id: kegg-database name: KEGG Database description: Programmatic access to KEGG via BioServices for pathway analysis, gene functions, and metabolic cross-referencing. category: Research requires: [] examples:

• Retrieve the metabolic pathway for the human ZAP70 gene from KEGG.
• Map these compound IDs to their corresponding KEGG pathway identifiers.

BioServices

Overview

BioServices is a Python package providing programmatic access to approximately 40 bioinformatics web services and databases. Retrieve biological data, perform cross-database queries, map identifiers, analyze sequences, and integrate multiple biological resources in Python workflows. The package handles both REST and SOAP/WSDL protocols transparently.

When to Use This Skill

This skill should be used when:

• Retrieving protein sequences, annotations, or structures from UniProt, PDB, Pfam
• Analyzing metabolic pathways and gene functions via KEGG or Reactome
• Searching compound databases (ChEBI, ChEMBL, PubChem) for chemical information
• Converting identifiers between different biological databases (KEGG↔UniProt, compound IDs)
• Running sequence similarity searches (BLAST, MUSCLE alignment)
• Querying gene ontology terms (QuickGO, GO annotations)
• Accessing protein-protein interaction data (PSICQUIC, IntactComplex)
• Mining genomic data (BioMart, ArrayExpress, ENA)
• Integrating data from multiple bioinformatics resources in a single workflow

Instruction

• Utilize the BioServices Python client to query KEGG REST services for genes, pathways, and compounds.
• Retrieve specific metabolic pathway maps and their associated interactions using gene symbols or identifiers.
• Perform identifier mapping between KEGG and other biological databases like UniProt or ChEMBL.
• Search for chemical compounds in ChEBI or PubChem and cross-reference them with KEGG metabolic pathways.
• Extract network interaction data for pathways to enable downstream topological analysis with tools like NetworkX.
• Execute batch ID conversion utilities to process large-scale genomic or metabolomic datasets efficiently.

Core Capabilities

1. Protein Analysis

Retrieve protein information, sequences, and functional annotations:

2. Pathway Discovery and Analysis

Access KEGG pathway information for genes and organisms:

Key methods:

• lookfor_organism(), lookfor_pathway(): Search by name
• get_pathway_by_gene(): Find pathways containing genes
• parse_kgml_pathway(): Extract structured pathway data
• pathway2sif(): Get protein interaction networks

Reference: references/workflow_patterns.md for complete pathway analysis workflows.

3. Compound Database Searches

Search and cross-reference compounds across multiple databases:

Common workflow:

1. Search compound by name in KEGG
2. Extract KEGG compound ID
3. Use UniChem for KEGG → ChEMBL mapping
4. ChEBI IDs are often provided in KEGG entries

4. Sequence Analysis

Run BLAST searches and sequence alignments:

Note: BLAST jobs are asynchronous. Check status before retrieving results.

5. Identifier Mapping

Convert identifiers between different biological databases:

6. Gene Ontology Queries

Access GO terms and annotations:

7. Protein-Protein Interactions

Available databases: MINT, IntAct, BioGRID, DIP, and 30+ others.

Multi-Service Integration Workflows

BioServices excels at combining multiple services for comprehensive analysis. Common integration patterns:

Complete Protein Analysis Pipeline

Execute a full protein characterization workflow:

This script demonstrates:

1. UniProt search for protein entry
2. FASTA sequence retrieval
3. BLAST similarity search
4. KEGG pathway discovery
5. PSICQUIC interaction mapping

Pathway Network Analysis

Analyze all pathways for an organism:

Extracts and analyzes:

• All pathway IDs for organism
• Protein-protein interactions per pathway
• Interaction type distributions
• Exports to CSV/SIF formats

Cross-Database Compound Search

Map compound identifiers across databases:

Retrieves:

• KEGG compound ID
• ChEBI identifier
• ChEMBL identifier
• Basic compound properties

Batch Identifier Conversion

Convert multiple identifiers at once:

Best Practices

Output Format Handling

Different services return data in various formats:

• XML: Parse using BeautifulSoup (most SOAP services)
• Tab-separated (TSV): Pandas DataFrames for tabular data
• Dictionary/JSON: Direct Python manipulation
• FASTA: BioPython integration for sequence analysis

Rate Limiting and Verbosity

Control API request behavior:

Error Handling

Wrap service calls in try-except blocks:

Organism Codes

Use standard organism abbreviations:

• hsa: Homo sapiens (human)
• mmu: Mus musculus (mouse)
• dme: Drosophila melanogaster
• sce: Saccharomyces cerevisiae (yeast)

List all organisms: k.list("organism") or k.organismIds

Integration with Other Tools

BioServices works well with:

• BioPython: Sequence analysis on retrieved FASTA data
• Pandas: Tabular data manipulation
• PyMOL: 3D structure visualization (retrieve PDB IDs)
• NetworkX: Network analysis of pathway interactions
• Galaxy: Custom tool wrappers for workflow platforms

Output

• Formatted reports on gene functions, pathway memberships, and compound properties.
• Network interaction files and metabolic maps for the queried biological entities.
• Automated Python scripts for bulk data retrieval and identifier conversion.