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)
• 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 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
• 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:

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:

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

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:

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]

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:

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