/bioservices

name: bioservices
description: Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
license: GPLv3 license
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.9–3.12 and internet access to 40+ bioinformatics web APIs. NCBI BLAST requires a contact email (`NCBI_EMAIL` env var or explicit parameter).
metadata:
  version: "1.4"
  skill-author: K-Dense Inc.
  openclaw:
    envVars:
    - name: NCBI_EMAIL
      required: false
      description: Email for NCBI service identification.

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

**Version caveat:** the per-source `get_compound_id_from_*` helpers are gone from bioservices 1.16.0 — check `hasattr(u, "get_compound_id_from_kegg")` first, and otherwise use the current UniChem API (`u.get_compounds(compound, source_type)` and read `res["compounds"][0]["sources"]`). ChEMBL lookups follow the same rule: `get_molecule`, not the pre-1.6 `get_compound_by_chemblId`.

**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 NCBI_EMAIL=you@lab.org

# 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