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预测发芽酵母中的动态表达模式,使用真菌DNA语言模型

Kuan-Hao Chao^1,2, Majed Mohamed Magzoub³, Emily Stoops³

¹Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.

Research square

|November 24, 2025

概括

我们开发了Shorkie,一种新的DNA语言模型,用于从DNA序列中预测基因表达. 该模型通过利用进化预训练和转移学习,显著增强基因表达预测和调控网络分析.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
分子生物学分子生物学

背景情况:

从DNA序列预测基因表达是复杂的,因为复杂的调节机制.
了解基因调节对于破译非编码变体和生物网络至关重要.

研究的目的:

从DNA序列开发一种改进基因表达预测的模型.
识别和分析基因调节中的调控语法和动图使用.
准确预测遗传变异对基因表达的影响.

主要方法:

在165个真菌基因组上训练一个面具DNA语言模型.
在酵母RNA-seq数据上微调语言模型,包括时间过程诱导实验.
评估模型在基因表达预测,cis-eQTL分类和记者测试中的表现.

主要成果:

与基线模型相比,Shorkie显著改善了基因表达预测.
该模型确定了保存的转录因子结合动机及其使用动态.
肖尔基准确地预测了变体效应,超过了现有的序列表达模型.

结论:

进化规模的预训练与转移学习相结合,增强了从序列对基因调节的解码.
肖尔基为非编码变体和监管网络动态提供了有价值的见解.
该框架为理解促销者动态,拼接和图案使用提供了一种强大的方法.

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