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通过大型语言模型增强基因组过度代表性分析

Jiqing Zhu¹, Rebecca Y Wang¹, Xiaoting Wang¹

¹Alector, Inc, South San Francisco, CA 94080, United States.

Bioinformatics advances

|May 22, 2025

概括

本研究介绍了llm2geneset,这是一种使用大型语言模型 (LLM) 来动态创建基因组数据库以分析高通量生物数据的新框架. 这种方法提供了灵活,上下文意识的解释,匹配人类策划的基因组质量.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

传统的基因组过度代表性分析 (ORA) 依赖于静态的,由人类策划的数据库,限制了解释高吞吐量转录组学和蛋白质组学数据的灵活性.
现有的方法很难适应特定的生物环境或动态生成的基因列表.

研究的目的:

开发一个灵活的框架,llm2geneset,利用大型语言模型 (LLM) 来动态生成基因组数据库.
通过将LLM生成的基因组与ORA等分析方法相结合,实现对生物数据的上下文感知功能解释.
为了对LLM生成的基因组与人类策划的数据库的性能进行基准测试.

主要方法:

开发llm2geneset框架,利用LLM来创建基于输入基因和自然语言生物背景的基因组.
集成动态生成的基因组与已建立的分析方法,如ORA,用于功能注释.
对LLM生成的基因组进行比较分析,并使用基准研究对人类策划的数据库进行比较.
该框架应用于与TREM2激动剂治疗的iPSC衍生微质细胞的RNA测序数据.

主要成果:

通过LLM生成的基因组表现出与人类策划的基因组相当的质量.
llm2geneset框架成功地识别了输入基因组中的生物过程,超过了传统的ORA和直接的LLM提示.
该框架促进了灵活的,对上下文有意识的基因组生成,并改善了高通量生物数据的解释,正如TREM2激动剂研究所显示的那样.

结论:

llm2geneset为传统的基因组丰富分析提供了强大而灵活的替代方案,利用LLMs进行动态数据库生成.
该框架通过提供特定上下文的功能注释来增强复杂生物数据集的解释.
llm2geneset代表了生物信息学工具的重大进步,用于生物数据分析和发现.

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