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.
OLAF: An Open Life Science Analysis Framework for Conversational Bioinformatics Powered by Large Language Models
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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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From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics
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.