BioServices

Overview

BioServices is a Python package providing programmatic access to roughly 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.

Recent changes that break old scripts (verified against bioservices 1.16.0):

• SOAP/WSDL support was removed in 1.15 — every active service is REST now, and the

WSDLService class and its suds dependency are gone.

• `PSICQUIC` and `BioGRID` were removed in 1.14. For protein interactions use the

STRING class (added in 1.14) or IntactComplex; from bioservices import PSICQUIC raises ImportError.

• `UniProt.mapping()` returns the raw UniProt job payload —

{"results": [{"from": ..., "to": ...}, ...], "failedIds": [...]} — not a {source_id: [target_ids]} dict. See "Identifier Mapping" below.

• NCBIblast methods are snake_case (get_status, get_result, get_result_types,

wait); the old getStatus/getResult camelCase names are gone. 1.16 also adds ncbiblastapi.NCBIBlastAPI, which submits to NCBI directly instead of EBI.

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 (STRING, IntactComplex)
• Mining genomic data (BioMart, ArrayExpress, ENA)
• Integrating data from multiple bioinformatics resources in a single workflow

Does NOT Trigger

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. frmt is one of xlsx/fasta/json/gff/tsv — "tab" was
# retired with the June-2022 UniProt API and now raises.
results = u.search("ZAP70_HUMAN", frmt="tsv", columns="accession,gene_names,organism_name")

# Retrieve FASTA sequence (frmt defaults to json)
sequence = u.retrieve("P43403", frmt="fasta")

# Map identifiers between databases -> {"results": [{"from": ..., "to": ...}], "failedIds": [...]}
job = u.mapping(fr="UniProtKB_AC-ID", to="KEGG", query="P43403")
kegg_ids = [r["to"] for r in job["results"]]

Key methods:

• search(): Query UniProt with flexible search terms (frmt="tsv", columns as UniProt

return-field names such as accession, gene_names, organism_name, length)

• retrieve(): Get protein entries in various formats (json, txt, xml, rdf, gff, fasta)
• mapping(): Submit an ID-mapping job and return its results payload

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

k = KEGG()

# Search compounds by name — the tab-separated result rows are "C11222\tGeldanamycin"
results = k.find("compound", "Geldanamycin")

# Get compound information with database links
compound_info = k.get("cpd:C11222")  # Includes ChEBI links

# Cross-reference KEGG compound → ChEBI (KEGG→ChEMBL has no direct API)
mapping = k.conv("chebi", "compound")
mapping["cpd:C11222"]   # -> 'chebi:5292'  (Geldanamycin)

Common workflow:

1. Search compound by name in KEGG
2. Extract KEGG compound ID
3. Use KEGG.conv for KEGG → ChEBI mapping (ChEBI IDs are also embedded in KEGG entries)
4. If a ChEMBL ID is required, obtain it via a separate route (the ChEMBL web service / chembl_webresource_client, or the live UniChem REST API directly) — there is no bioservices UniChem convenience method for KEGG → ChEMBL

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 via the EBI job service
jobid = s.run(
    program="blastp",
    sequence=protein_sequence,
    stype="protein",
    database="uniprotkb",
    email="[email protected]"  # a real address is required; jobs are killed without one
)

# Poll, then fetch. Method names are snake_case since the API refresh.
s.wait(jobid)                    # blocks until the job leaves RUNNING
status = s.get_status(jobid)     # RUNNING | FINISHED | ERROR | FAILURE | NOT_FOUND
results = s.get_result(jobid, "out")
print(s.get_result_types(jobid))  # what formats this job can return

BLAST jobs are asynchronous — check the status (or call wait) before retrieving results. For jobs submitted to NCBI rather than EBI, bioservices 1.16 adds from bioservices import NCBIBlastAPI with the same run/get_status/get_result shape.

5. Identifier Mapping

Convert identifiers between different biological databases:

from bioservices import UniProt, KEGG

# UniProt mapping (many database pairs supported)
u = UniProt()
job = u.mapping(
    fr="UniProtKB_AC-ID",  # Source database
    to="KEGG",              # Target database
    query="P43403"          # Identifier(s) to convert; a list is also accepted
)

# The payload is {"results": [{"from": ..., "to": ...}], "failedIds": [...]}.
# Collapse it yourself when you want a per-source-ID dict:
from collections import defaultdict

mapped = defaultdict(list)
for row in job["results"]:
    mapped[row["from"]].append(row["to"])

# KEGG gene ID -> UniProt. Non-UniProt sources may only map *to* UniProtKB,
# so "KEGG" -> "UniProtKB" is valid while "KEGG" -> "UniProtKB_AC-ID" is not.
kegg_to_uniprot = u.mapping(fr="KEGG", to="UniProtKB", query="hsa:7535")

# For compounds, map KEGG → ChEBI via KEGG.conv
# (KEGG → ChEMBL has no direct API; obtain ChEMBL IDs separately
#  via the ChEMBL web service / chembl_webresource_client or the
#  live UniChem REST API directly)
k = KEGG()
kegg_to_chebi = k.conv("chebi", "compound")
chebi_from_kegg = kegg_to_chebi["cpd:C11222"]  # -> 'chebi:5292'

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 (returns parsed JSON from the QuickGO REST API)
term_info = g.get_go_terms("GO:0003824")
ancestors = g.get_go_ancestors("GO:0003824")

# Annotations: the parameters follow the QuickGO REST API, not the old
# protein=/format= signature. geneProductId is prefixed, limit is capped at 100.
annotations = g.Annotation(
    geneProductId="UniProtKB:P43403",
    includeFields="goName",
    limit=100,
    page=1,
)
for row in annotations["results"][:5]:
    print(row["goId"], row["goName"], row["goAspect"])

Annotation returns a dict with numberOfHits and results; page through it rather than raising limit (values above 100 raise a TypeError).

7. Protein-Protein Interactions

PSICQUIC and BioGRID were removed from bioservices in 1.14. Use the STRING service (or IntactComplex for curated complexes):

from bioservices import STRING

s = STRING()

# Functional + physical partners of a protein
partners = s.get_interaction_partners("ZAP70", species=9606, limit=20)

# Interactions within a given set of proteins
network = s.get_interactions(["ZAP70", "CD247", "LCK"], species=9606)

for row in partners:
    print(row["preferredName_A"], row["preferredName_B"], row["score"])

network_type="physical" restricts to physical complexes; required_score (0–1000) sets the confidence floor. STRING scores are 0–1 in the JSON output.

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 [email protected]

This script demonstrates:

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

Resources

scripts/

Executable Python scripts demonstrating complete workflows:

• protein_analysis_workflow.py: End-to-end protein characterization
• pathway_analysis.py: KEGG pathway discovery and network extraction
• compound_cross_reference.py: Multi-database compound searching
• batch_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 methods
• workflow_patterns.md: Detailed multi-step analysis workflows
• identifier_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

Part of the AlterLab Academic Skills suite.