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Prompt-based bioinformatic pipeline generation for a multi-step metaviral workflow.

Pengchong Ma1, Haoze Zheng1, Weijun Yi2,3

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Large language models (LLMs) can generate complex bioinformatics pipelines, aiding researchers without extensive programming skills. Advanced models like ChatGPT-4 and Gemini 2.5 show superior performance in creating and updating these automated workflows.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Artificial Intelligence

Background:

  • The increasing complexity of bioinformatics tools and multi-step analytical procedures challenges the creation of effective computational pipelines.
  • Researchers, especially those with limited programming expertise, face significant hurdles in developing and maintaining these workflows.

Purpose of the Study:

  • To investigate the potential of large language models (LLMs) in generating end-to-end bioinformatics pipelines.
  • To evaluate the effectiveness of various LLMs in creating automated analytical workflows using a multi-step metaviral workflow as a case study.

Main Methods:

  • Testing multiple large language models, including OpenAI's ChatGPT series, Anthropic's Claude series, Google Gemini, Meta Llama, and DeepSeek.
  • Utilizing carefully crafted prompts and incorporating official documentation to guide LLM performance.
  • Assessing pipeline generation success rates and the models' ability to handle tool substitutions.

Main Results:

  • ChatGPT-4, ChatGPT-5, Claude 4.5, and Gemini 2.5 demonstrated statistically significant superior performance in generating complete bioinformatics pipelines.
  • These leading LLMs effectively managed tool substitutions and benefited from prompt engineering and documentation integration.
  • All tested LLMs showed potential for both initial pipeline generation and subsequent updates.

Conclusions:

  • LLMs offer a promising solution for automating the construction of bioinformatics pipelines, democratizing complex analyses.
  • Prompt engineering and leveraging tool documentation are key strategies for maximizing LLM effectiveness in bioinformatics pipeline generation.
  • The study provides a foundation for using AI to streamline bioinformatics workflow development and maintenance.