bioservices

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:

• 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

1. Protein Analysis

Retrieve protein information, sequences, and functional annotations:

```python

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:

```python

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:

```python

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:

```python

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:

```python

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:

```python

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:

```python

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:

Complete Protein Analysis Pipeline

Execute a full protein characterization workflow:

```bash

python scripts/protein_analysis_workflow.py ZAP70_HUMAN your.email@example.com

```

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:

```bash

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:

```bash

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:

```bash

python scripts/batch_id_converter.py input_ids.txt --from UniProtKB_AC-ID --to KEGG

```

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:

```python

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:

```python

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

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.

```bash

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:

• Official documentation: https://bioservices.readthedocs.io/
• Source code: https://github.com/cokelaer/bioservices
• Service-specific references in references/services_reference.md