ArticleBioinformatics advances2026
Prompt-based bioinformatic pipeline generation for a multi-step metaviral workflow.
Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
8 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.