bioservices
GitHub提供40+生物信息数据库的统一Python接口，支持跨库查询、ID映射及序列分析。适用于需整合UniProt、KEGG等多源数据的复杂工作流，区别于单一查询工具。
触发场景
安装
npx skills add synthetic-sciences/openscience --skill bioservices -g -y
SKILL.md
Frontmatter
{
"name": "bioservices",
"license": "GPLv3 license",
"category": "biology",
"metadata": {
"skill-author": "Synthetic Sciences"
},
"description": "Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence\/file manipulation use biopython."
}
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
Core Capabilities
1. Protein Analysis
Retrieve protein information, sequences, and functional annotations:
from bioservices import UniProt
u = UniProt(verbose=False)
# Search for protein by name
results = u.search("ZAP70_HUMAN", frmt="tab", columns="id,genes,organism")
# Retrieve FASTA sequence
sequence = u.retrieve("P43403", "fasta")
# Map identifiers between databases
kegg_ids = u.mapping(fr="UniProtKB_AC-ID", to="KEGG", query="P43403")
Key methods:
search(): Query UniProt with flexible search termsretrieve(): Get protein entries in various formats (FASTA, XML, tab)mapping(): Convert identifiers between databases
Reference: references/services_reference.md for complete UniProt API details.
2. Pathway Discovery and Analysis
Access KEGG pathway information for genes and organisms:
from bioservices import KEGG
k = KEGG()
k.organism = "hsa" # Set to human
# Search for organisms
k.lookfor_organism("droso") # Find Drosophila species
# Find pathways by name
k.lookfor_pathway("B cell") # Returns matching pathway IDs
# Get pathways containing specific genes
pathways = k.get_pathway_by_gene("7535", "hsa") # ZAP70 gene
# Retrieve and parse pathway data
data = k.get("hsa04660")
parsed = k.parse(data)
# Extract pathway interactions
interactions = k.parse_kgml_pathway("hsa04660")
relations = interactions['relations'] # Protein-protein interactions
# Convert to Simple Interaction Format
sif_data = k.pathway2sif("hsa04660")
Key methods:
lookfor_organism(),lookfor_pathway(): Search by nameget_pathway_by_gene(): Find pathways containing genesparse_kgml_pathway(): Extract structured pathway datapathway2sif(): 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:
from bioservices import KEGG, UniChem
k = KEGG()
# Search compounds by name
results = k.find("compound", "Geldanamycin") # Returns cpd:C11222
# Get compound information with database links
compound_info = k.get("cpd:C11222") # Includes ChEBI links
# Cross-reference KEGG → ChEMBL using UniChem
u = UniChem()
chembl_id = u.get_compound_id_from_kegg("C11222") # Returns CHEMBL278315
Common workflow:
- Search compound by name in KEGG
- Extract KEGG compound ID
- Use UniChem for KEGG → ChEMBL mapping
- ChEBI IDs are often provided in KEGG entries
Reference: references/identifier_mapping.md for complete cross-database mapping guide.
4. Sequence Analysis
Run BLAST searches and sequence alignments:
from bioservices import NCBIblast
s = NCBIblast(verbose=False)
# Run BLASTP against UniProtKB
jobid = s.run(
program="blastp",
sequence=protein_sequence,
stype="protein",
database="uniprotkb",
email="your.email@example.com" # Required by NCBI
)
# Check job status and retrieve results
s.getStatus(jobid)
results = s.getResult(jobid, "out")
Note: BLAST jobs are asynchronous. Check status before retrieving results.
5. Identifier Mapping
Convert identifiers between different biological databases:
from bioservices import UniProt, KEGG
# UniProt mapping (many database pairs supported)
u = UniProt()
results = u.mapping(
fr="UniProtKB_AC-ID", # Source database
to="KEGG", # Target database
query="P43403" # Identifier(s) to convert
)
# KEGG gene ID → UniProt
kegg_to_uniprot = u.mapping(fr="KEGG", to="UniProtKB_AC-ID", query="hsa:7535")
# For compounds, use UniChem
from bioservices import UniChem
u = UniChem()
chembl_from_kegg = u.get_compound_id_from_kegg("C11222")
Supported mappings (UniProt):
- UniProtKB ↔ KEGG
- UniProtKB ↔ Ensembl
- UniProtKB ↔ PDB
- UniProtKB ↔ RefSeq
- And many more (see
references/identifier_mapping.md)
6. Gene Ontology Queries
Access GO terms and annotations:
from bioservices import QuickGO
g = QuickGO(verbose=False)
# Retrieve GO term information
term_info = g.Term("GO:0003824", frmt="obo")
# Search annotations
annotations = g.Annotation(protein="P43403", format="tsv")
7. Protein-Protein Interactions
Query interaction databases via PSICQUIC:
from bioservices import PSICQUIC
s = PSICQUIC(verbose=False)
# Query specific database (e.g., MINT)
interactions = s.query("mint", "ZAP70 AND species:9606")
# List available interaction databases
databases = s.activeDBs
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:
python scripts/protein_analysis_workflow.py ZAP70_HUMAN your.email@example.com
This script demonstrates:
- UniProt search for protein entry
- FASTA sequence retrieval
- BLAST similarity search
- KEGG pathway discovery
- PSICQUIC interaction mapping
Pathway Network Analysis
Analyze all pathways for an organism:
python scripts/pathway_analysis.py hsa output_directory/
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:
python scripts/compound_cross_reference.py Geldanamycin
Retrieves:
- KEGG compound ID
- ChEBI identifier
- ChEMBL identifier
- Basic compound properties
Batch Identifier Conversion
Convert multiple identifiers at once:
python scripts/batch_id_converter.py input_ids.txt --from UniProtKB_AC-ID --to KEGG
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:
from bioservices import KEGG
k = KEGG(verbose=False) # Suppress HTTP request details
k.TIMEOUT = 30 # Adjust timeout for slow connections
Error Handling
Wrap service calls in try-except blocks:
try:
results = u.search("ambiguous_query")
if results:
# Process results
pass
except Exception as e:
print(f"Search failed: {e}")
Organism Codes
Use standard organism abbreviations:
hsa: Homo sapiens (human)mmu: Mus musculus (mouse)dme: Drosophila melanogastersce: 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
Resources
scripts/
Executable Python scripts demonstrating complete workflows:
protein_analysis_workflow.py: End-to-end protein characterizationpathway_analysis.py: KEGG pathway discovery and network extractioncompound_cross_reference.py: Multi-database compound searchingbatch_id_converter.py: Bulk identifier mapping utility
Scripts can be executed directly or adapted for specific use cases.
references/
Detailed documentation loaded as needed:
services_reference.md: Comprehensive list of all 40+ services with methodsworkflow_patterns.md: Detailed multi-step analysis workflowsidentifier_mapping.md: Complete guide to cross-database ID conversion
Load references when working with specific services or complex integration tasks.
Installation
uv pip install bioservices
Dependencies are automatically managed. Package is tested on Python 3.9-3.12.
Additional Information
For detailed API documentation and advanced features, refer to:
- Official documentation: https://bioservices.readthedocs.io/
- Source code: https://github.com/cokelaer/bioservices
- Service-specific references in
references/services_reference.md
版本历史
- e9844a4 当前 2026-07-11 17:19
