Primary Python tool for 40+ bioinformatics services. Preferred for multi-database workflows: UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO. Unified API for queries, ID mapping, pathway analysis. For direct REST control, use individual database skills (uniprot-database, kegg-database).
npxskills add davila7/claude-code-templates--skill bioservicesLoading…
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.
This skill should be used when:
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 databasesReference: references/services_reference.md for complete UniProt API details.
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 networksReference: references/workflow_patterns.md for complete pathway analysis workflows.
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:
Reference: references/identifier_mapping.md for complete cross-database mapping guide.
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="[email protected]" # 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.
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):
references/identifier_mapping.md)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")
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.
BioServices excels at combining multiple services for comprehensive analysis. Common integration patterns:
Execute a full protein characterization workflow:
python scripts/protein_analysis_workflow.py ZAP70_HUMAN [email protected]
This script demonstrates:
Analyze all pathways for an organism:
python scripts/pathway_analysis.py hsa output_directory/
Extracts and analyzes:
Map compound identifiers across databases:
python scripts/compound_cross_reference.py Geldanamycin
Retrieves:
Convert multiple identifiers at once:
python scripts/batch_id_converter.py input_ids.txt --from UniProtKB_AC-ID --to KEGG
Different services return data in various formats:
Control API request behavior:
from bioservices import KEGG
k = KEGG(verbose=False) # Suppress HTTP request details
k.TIMEOUT = 30 # Adjust timeout for slow connections
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}")
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
BioServices works well with:
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 utilityScripts can be executed directly or adapted for specific use cases.
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 conversionLoad references when working with specific services or complex integration tasks.
uv pip install bioservices
Dependencies are automatically managed. Package is tested on Python 3.9-3.12.
For detailed API documentation and advanced features, refer to:
references/services_reference.md
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