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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

Dylan Riffle , Nima Shirooni , Cody He , Manush Murali , Sovit Nayak , Rishikumar Gopalan , Diego Gonzalez Lopez This is my paper · ORCID
classification q-bio.QMcs.AIq-bio.GN
keywords olafbioinformaticsdatalanguagelifescienceanalysesanalysis
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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 2 Pith papers

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

  1. From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics

    cs.SE 2025-07 conditional novelty 5.0 of 10

    A qualitative study of ten bioinformatics workflows finds LLMs can generate usable Galaxy and Nextflow pipelines, with Gemini best for Galaxy and DeepSeek-V3 best for Nextflow.

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

    cs.CL 2025-11 reject novelty 4.0 of 10

    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.

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