bioservices

name	bioservices
description	Unified Python interface to 40+ bioinformatics services (UniProt, KEGG, ChEMBL, Reactome, PSICQUIC). Best for cross-database analysis, ID mapping, and multi-service workflows. For quick single-database lookups use gget.
version	1.0.0
allowed-tools	Read, Grep, Glob, Bash

BioServices

Python package providing programmatic access to ~40 bioinformatics web services. Handles REST and SOAP protocols transparently.

Installation

uv pip install bioservices

When to Use

• Querying multiple databases in a single workflow (UniProt + KEGG + ChEMBL)
• ID mapping across databases (UniProt ↔ KEGG ↔ Ensembl ↔ PDB)
• Pathway analysis (KEGG, Reactome)
• Protein-protein interaction queries (PSICQUIC: MINT, IntAct, BioGRID, 30+ others)
• Gene ontology lookups (QuickGO)

For quick single-database lookups, prefer gget.

Supporting Files

• services_reference.md - All 40+ services with methods
• workflow_patterns.md - Multi-step analysis workflows
• identifier_mapping.md - Cross-database ID conversion guide

Scripts

• scripts/protein_analysis_workflow.py - End-to-end protein characterization
• scripts/pathway_analysis.py - KEGG pathway network extraction
• scripts/compound_cross_reference.py - Multi-database compound search
• scripts/batch_id_converter.py - Bulk identifier mapping

Core Capabilities

1. Protein Analysis (UniProt)

from bioservices import UniProt

u = UniProt(verbose=False)

# Search by name
results = u.search("ZAP70_HUMAN", frmt="tab", columns="id,genes,organism")

# Retrieve FASTA
sequence = u.retrieve("P43403", "fasta")

# Map identifiers
kegg_ids = u.mapping(fr="UniProtKB_AC-ID", to="KEGG", query="P43403")

Supported mappings: UniProtKB ↔ KEGG, Ensembl, PDB, RefSeq, and many more.

2. Pathway Analysis (KEGG)

from bioservices import KEGG

k = KEGG()
k.organism = "hsa"

# Find pathways containing a gene
pathways = k.get_pathway_by_gene("7535", "hsa")  # ZAP70

# Parse pathway data
data = k.get("hsa04660")
parsed = k.parse(data)

# Extract interactions
interactions = k.parse_kgml_pathway("hsa04660")
sif_data = k.pathway2sif("hsa04660")  # Simple Interaction Format

3. Compound Cross-Referencing

from bioservices import KEGG, UniChem

k = KEGG()
results = k.find("compound", "Geldanamycin")  # → cpd:C11222

# KEGG → ChEMBL via UniChem
u = UniChem()
chembl_id = u.get_compound_id_from_kegg("C11222")

4. Sequence Analysis (BLAST)

from bioservices import NCBIblast

s = NCBIblast(verbose=False)
jobid = s.run(program="blastp", sequence=protein_sequence,
              stype="protein", database="uniprotkb",
              email="[e-mail]")
s.getStatus(jobid)  # Async - check status first
results = s.getResult(jobid, "out")

5. Gene Ontology (QuickGO)

from bioservices import QuickGO

g = QuickGO(verbose=False)
term_info = g.Term("GO:0003824", frmt="obo")
annotations = g.Annotation(protein="P43403", format="tsv")

6. Protein-Protein Interactions (PSICQUIC)

from bioservices import PSICQUIC

s = PSICQUIC(verbose=False)
interactions = s.query("mint", "ZAP70 AND species:9606")
databases = s.activeDBs  # 30+ databases

Best Practices

• Set verbose=False to suppress HTTP request details
• Adjust timeout for slow connections: k.TIMEOUT = 30
• Wrap service calls in try-except (APIs can be flaky)
• Organism codes: hsa (human), mmu (mouse), dme (fly), sce (yeast). List all: k.list("organism")
• Works well with BioPython (sequences), Pandas (tabular data), NetworkX (pathway networks)

Attribution

Adapted from K-Dense-AI/claude-scientific-skills (GPLv3). Docs: https://bioservices.readthedocs.io/ | Source: https://github.com/cokelaer/bioservices