bioservices

Access and integrate over 40 bioinformatics databases via Python interfaces.

Updated May 10, 2026

Search Tags:#bioinformatics #pathway analysis #network analysis #ID mapping #databases #protein sequences

Ready to run

npx skills add https://github.com/Imad-Oute/ResearchForge --skill bioservices-imad-oute

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bioservices
Source: https://github.com/Imad-Oute/ResearchForge/tree/main/OpenSource-Projects/claude-scientific-skills/scientific-skills/bioservices
Command: npx skills add https://github.com/Imad-Oute/ResearchForge --skill bioservices-imad-oute

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, xmltodict, pandas, and includes scripts (resource) and references (resource) components.

What problem does it solve?

bioservices provides Python tools for programmatic access to over 40 biological data resources, streamlining data retrieval, conversion, and analysis tasks.

Core Features & Use Cases

Data Integration: Combine data from multiple bioinformatics databases like UniProt, KEGG, ChEMBL, and others within Python workflows.
Identifier Mapping: Convert IDs between databases such as UniProt, KEGG, ChEBI, and PDB to facilitate cross-referencing.
Sequence and Pathway Analysis: Retrieve protein sequences, explore metabolic pathways, and analyze interaction networks automatically.
Use Case: A researcher builds a pipeline to identify protein functions, maps them to pathways, and visualizes the interaction network—all within Python scripts.

Quick Start

Use bioservices to fetch the sequence for a given UniProt ID and find related pathways straightforwardly.

Frequently Asked Questions about bioservices

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

FAQPage Schema

How do I convert protein IDs between UniProt and KEGG in Python?▼

Identifier mapping between UniProt and KEGG is handled by Python interfaces that query REST APIs to convert IDs across biological databases for cross-referencing. This allows you to translate identifiers seamlessly within data analysis pipelines.

Can I retrieve protein sequences and explore metabolic pathways automatically?▼

Retrieving protein sequences and exploring metabolic pathways is supported through automated bioinformatics data access leveraging REST APIs. You can fetch sequences for a given UniProt ID and find related pathways within Python scripts.

How do I integrate data from multiple bioinformatics databases like ChEMBL and PDB?▼

Data integration from multiple bioinformatics databases like ChEMBL and PDB is achieved by providing Python interfaces for over 40 biological data resources. This streamlines data retrieval and combination within automated research workflows.

Do I need the requests and pandas libraries to run bioinformatics data analysis pipelines?▼

The requests and pandas libraries are required dependencies to interact with web services efficiently and parse responses. These parsing libraries enable the Python interfaces to access biological data resources and support functional annotation workflows.

What's the best way to analyze interaction networks and map them to pathways using Python?▼

Analyzing interaction networks and mapping them to pathways is best done by leveraging REST APIs to support pathway exploration and network analysis automatically. You can build a pipeline to identify protein functions and visualize interaction networks within Python.

Are there limitations when using REST APIs for cross-database ID conversion in bioinformatics?▼

Cross-database ID conversion via REST APIs is limited by the availability and response format of external biological data resources. The Skill uses parsing libraries to interact efficiently, but requires stable network connections to access web services.