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

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arxiv 2504.03976 v2 pith:XH3EONFC submitted 2025-04-04 q-bio.QM cs.AIq-bio.GN

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

Dylan Riffle , Nima Shirooni , Cody He , Manush Murali , Sovit Nayak , Rishikumar Gopalan , Diego Gonzalez Lopez This is my paper
classification q-bio.QM cs.AIq-bio.GN
keywords olafbioinformaticsdatalanguagelifescienceanalysesanalysis
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances

    cs.CL 2025-11 reject novelty 4.0

    Across the 68 papers it surveys, domain-specialized generative models usually outperform general-purpose LLMs on biological tasks, and agentic/conversational workflows are the least-covered topics.