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<p>Metabarcoding remains challenging due to incomplete taxonomic annotations and computationally intensive processes. We present DeepCOI, a large language model-based classifier pre-trained on seven million cytochrome c oxidase I gene sequences. DeepCOI enables fast and accurate taxonomic assignment across eight major phyla, achieving an AU-ROC of 0.958 and AU-PR of 0.897–outperforming existing methods while significantly reducing inference time. Additionally, DeepCOI demonstrates interpretability by identifying taxonomically informative sequence positions. By integrating large-scale datasets and self-supervised learning, DeepCOI enhances both the accuracy and efficiency of metabarcoding processes, providing a scalable solution for biodiversity assessment and environmental monitoring.</p>

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DeepCOI: a large language model-driven framework for fast and accurate taxonomic assignment in animal metabarcoding

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月17日

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  • Ho-Jin Gwak,
  • Mina Rho

摘要

Metabarcoding remains challenging due to incomplete taxonomic annotations and computationally intensive processes. We present DeepCOI, a large language model-based classifier pre-trained on seven million cytochrome c oxidase I gene sequences. DeepCOI enables fast and accurate taxonomic assignment across eight major phyla, achieving an AU-ROC of 0.958 and AU-PR of 0.897–outperforming existing methods while significantly reducing inference time. Additionally, DeepCOI demonstrates interpretability by identifying taxonomically informative sequence positions. By integrating large-scale datasets and self-supervised learning, DeepCOI enhances both the accuracy and efficiency of metabarcoding processes, providing a scalable solution for biodiversity assessment and environmental monitoring.