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Generación de pipelines bioinformáticos basados en prompts para un flujo de trabajo metaviral de varios pasos

Pengchong Ma¹, Haoze Zheng¹, Weijun Yi^2,3

¹School of Computing, University of Nebraska Lincoln, Lincoln, NE 68588, United States.

Bioinformatics advances

|January 12, 2026

Abstracto

Motivation:

The rapid evolution of bioinformatics tools and multi-step analytic procedure presents a challenge for building effective pipelines, particularly for researchers without extensive programming expertise. This study demonstrates that large language models (LLMs) hold strong potential for generating end-to-end bioinformatic pipelines through carefully crafted prompts, using a multi-step metaviral workflow as a representative example. Multiple LLMs were tested for their effectiveness, including OpenAI ChatGPT series, Anthropic Claude series, Google Gemini, Meta Llama, and DeepSeek.

Results:

Our results show that ChatGPT-4, ChatGPT-5, Claude 4.5, and Gemini 2.5 consistently outperform other LLMs in generating complete bioinformatic pipelines, with statistically significant success rates. These models also handle tool substitutions effectively. Simple prompt engineering and the inclusion of official documentation further enhance performance, especially for newer bioinformatic tools. While capabilities vary, all LLMs tested show potential for both pipeline generation and updates with our designed prompts and strategies.

Availability And Implementation:

All prompts are available in the paper. The examples are available in GitHub https://github.com/mpckkk/pBio.

Palabras clave:

modelos de lenguaje grandes LLM generación de pipelines bioinformáticos flujos de trabajo automatizados ingeniería de prompts documentación de herramientas biología computacional inteligencia artificial

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