bioservices

Query and integrate over 40 bioinformatics web services via a unified Python interface.

2|Updated Jun 4, 2026
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill bioservices-lord1egypt
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: bioservices
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/bioservices
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill bioservices-lord1egypt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires bioservices, biopython, pandas, networkx, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill solves the fragmentation of biological data by providing a single, consistent Python interface to over 40 bioinformatics web services, eliminating the need to learn dozens of disparate APIs.

Core Features & Use Cases

  • Cross-Database Integration: Seamlessly map identifiers between UniProt, KEGG, ChEMBL, and Reactome in a single workflow.
  • Comprehensive Analysis: Perform end-to-end protein characterization, including sequence retrieval, BLAST similarity searches, and pathway discovery.
  • Use Case: A researcher can use this skill to take a list of protein identifiers, map them to their corresponding KEGG pathways, and extract protein-protein interaction networks for downstream analysis without writing custom API wrappers.

Quick Start

Use the bioservices skill to map the UniProt ID P43403 to its corresponding KEGG gene identifier and retrieve its associated metabolic pathways.

Frequently Asked Questions about bioservices

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I query multiple bioinformatics databases for cross-database identifier mapping?

Cross-database identifier mapping uses a unified Python interface to query over 40 bioinformatics web services, mapping identifiers between UniProt, KEGG, ChEMBL, and Reactome in a single workflow.

What is the best way to integrate proteomics data and pathway discovery using Python?

Integrating proteomics data and pathway discovery is done by querying REST and SOAP protocols transparently, retrieving sequences, performing BLAST similarity searches, and extracting metabolic pathways.

Can I use this to retrieve protein-protein interaction networks without writing custom API wrappers?

Yes, you can retrieve protein-protein interaction networks directly, taking protein identifiers to map them to KEGG pathways and extract interaction networks for downstream analysis without custom API wrappers.

Do I need biopython and pandas to perform sequence analysis and data integration?

Yes, biopython and pandas are required dependencies to perform sequence analysis and data integration, alongside networkx for processing the extracted protein-protein interaction networks.

How does unified access to biological web services handle REST and SOAP protocols?

Unified access handles REST and SOAP protocols via the bioservices package, providing a consistent Python interface that eliminates the need to learn dozens of disparate APIs for data retrieval.

When should I not use a unified bioinformatics interface for database queries?

A unified bioinformatics interface is not ideal if your research requires highly specialized, non-standard database query parameters outside the scope of the 40 integrated web services and standard workflows.