---
name: bioservices
description: "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)."
---

# 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.

## 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:

```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.

## 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:

```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
```

## 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:

```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

## 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

```bash
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`