用语言模型分析微生物组数据，揭示生命语言的深层规律
Recent advances in deep learning and language models for studying the microbiome
- 将微生物基因序列类比为自然语言，应用大模型技术解析复杂生态
- 实现病毒组建模、新合成基因簇预测等关键任务，提升功能发现能力
- 适合生物信息学、合成生物学研究者参考，推动微生物组智能分析
深度学习，特别是大语言模型（LLMs）的最新进展，对微生物组和宏基因组数据分析产生了深远影响。微生物蛋白和基因序列如同自然语言，构成了生命的语言体系，使大模型能够从中提取有用洞察。本文综述了深度学习与语言模型在微生物组和宏基因组数据分析中的应用，重点探讨问题建模方式、所需数据集以及语言建模技术的整合。全面回顾了蛋白质/基因组语言建模的发展及其在微生物组研究中的贡献，还讨论了新型病毒组语言建模、生物合成基因簇预测及宏基因组知识整合等应用。
原文摘要 · Abstract (English)
Recent advancements in deep learning, particularly large language models (LLMs), made a significant impact on how researchers study microbiome and metagenomics data. Microbial protein and genomic sequences, like natural languages, form a language of life, enabling the adoption of LLMs to extract useful insights from complex microbial ecologies. In this paper, we review applications of deep learning and language models in analyzing microbiome and metagenomics data. We focus on problem formulations, necessary datasets, and the integration of language modeling techniques. We provide an extensive overview of protein/genomic language modeling and their contributions to microbiome studies. We also discuss applications such as novel viromics language modeling, biosynthetic gene cluster prediction, and knowledge integration for metagenomics studies.
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