OLAF: An Open Life Science Analysis Framework for Conversational Bioinformatics Powered by Large Language Models

TL;DR

OLAF is an open-source, AI-powered platform that simplifies bioinformatics analysis by integrating large language models with code execution, enabling non-programmers to perform complex scientific data analyses through a user-friendly web interface.

Contribution

OLAF introduces a modular architecture combining LLMs with code execution for accessible, reproducible bioinformatics workflows in a web-based platform.

Findings

Supports diverse bioinformatics analyses like single-cell RNA-seq and gene annotation.

Enables non-programmers to perform complex analyses via natural language.

Provides a reproducible environment integrating scientific libraries.

Abstract

OLAF (Open Life Science Analysis Framework) is an open-source platform that enables researchers to perform bioinformatics analyses using natural language. By combining large language models (LLMs) with a modular agent-pipe-router architecture, OLAF generates and executes bioinformatics code on real scientific data, including formats like .h5ad. The system includes an Angular front end and a Python/Firebase backend, allowing users to run analyses such as single-cell RNA-seq workflows, gene annotation, and data visualization through a simple web interface. Unlike general-purpose AI tools, OLAF integrates code execution, data handling, and scientific libraries in a reproducible, user-friendly environment. It is designed to lower the barrier to computational biology for non-programmers and support transparent, AI-powered life science research.