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.
SKILL.md
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.
Version note: Examples target bioservices 1.16.0 (PyPI, Mar 2026). Requires Python 3.9–3.12. UniProt REST changes in mid-2022 (bioservices ≥1.10) mainly affect tabular columns names — see upstream _legacy_names if parsing breaks. ChEMBL wrappers changed at 1.6.0 (2018 API); use get_similarity, get_substructure, get_molecule instead of pre-1.6 method names.
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)
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 terms
retrieve(): 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 name
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:
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
Version caveat: the per-source get_compound_id_from_* helpers are gone from
bioservices 1.16.0 — check hasattr(u, "get_compound_id_from_kegg") first, and
otherwise use the current UniChem API (u.get_compounds(compound, source_type)
and read res["compounds"][0]["sources"]). ChEMBL lookups follow the same rule:
get_molecule, not the pre-1.6 get_compound_by_chemblId.
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. NCBI requires a contact email — prefer the NCBI_EMAIL environment variable (same convention as BioPython Entrez and other repo skills):
import os
from bioservices import NCBIblast
s = NCBIblast(verbose=False)
email = os.environ["NCBI_EMAIL"] # set before running: export [email protected]
# Run BLASTP against UniProtKB
jobid = s.run(
program="blastp",
sequence=protein_sequence,
stype="protein",
database="uniprotkb",
email=email,
)
# 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. PSICQUIC is not shipped by every
release — it is absent from 1.16.0 — so import it defensively and fall back to
IntactComplex, OmniPath, or STRING when it is missing:
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:
export [email protected]
python scripts/protein_analysis_workflow.py ZAP70_HUMAN
# Or pass email as optional second argument if NCBI_EMAIL is unset
python scripts/protein_analysis_workflow.py ZAP70_HUMAN [email protected]
