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

• URL: http://arxiv.org/abs/2504.03976v2
• Date: Thu, 10 Apr 2025 19:32:47 GMT
• Title: OLAF: An Open Life Science Analysis Framework for Conversational Bioinformatics Powered by Large Language Models
• Authors: Dylan Riffle, Nima Shirooni, Cody He, Manush Murali, Sovit Nayak, Rishikumar Gopalan, Diego Gonzalez Lopez,
• Abstract summary: OLAF (Open Life Science Analysis Framework) is an open-source platform that enables researchers to perform bioinformatics analyses using natural language.<n>By combining large language models (LLMs) with a modular agent-pipe-router architecture, OLAF generates and executes bioinformatics code on real scientific data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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