Decoding Prokaryotic Whole Genomes with a Product-Contextualized Large Language Model Article Swipe

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Shiwen Ni , Shuaimin Li , Shijian Wang , Xinyu Bi , Y. Li , Chai Phei Gan , iarui Jin , Yuan Lü , Min Yang , Teng Wang ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.64898/2025.12.03.692003

Genomes encode the instructions for life, yet their full interpretation requires models capable of capturing long-range context and functional meaning at scale. Existing genome language models (gLMs) are limited by short context windows, high computational cost, and poor interpretability. We present GenSyntax, a product-contextualized large language model (LLM) trained on 49,250 annotated prokaryotic genomes. GenSyntax replaces nucleotide tokenization with gene product descriptors, transforming genomes into “genetic paragraphs” that preserve functional semantics. Using a two-stage training strategy, GenSyntax achieves leading performance in plasmid host identification, gene function prediction, genome assembly, and gene essentiality assessment compared with the other LLMs. It also enables phenotype prediction and minimal genome design, establishing a scalable and interpretable framework for genome-scale decoding and synthetic biology.

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Type: article
Landing Page: https://doi.org/10.64898/2025.12.03.692003
OA Status: gold
References: 26
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DOI: https://doi.org/10.64898/2025.12.03.692003

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Title: Decoding Prokaryotic Whole Genomes with a Product-Contextualized Large Language Model

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Publication year: 2025

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Publication date: 2025-12-05

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Authors: Shiwen Ni, Shuaimin Li, Shijian Wang, Xinyu Bi, Y. Li, Chai Phei Gan, iarui Jin, Yuan Lü, Min Yang, Teng Wang

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Landing page: https://doi.org/10.64898/2025.12.03.692003

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OA status: gold

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OA URL: https://doi.org/10.64898/2025.12.03.692003

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Cited by: 0

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References (count): 26

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