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短文本调控DNA语言模型与动机发现规范化
bioRxiv : the preprint server for biology
|February 12, 2026
概括
我们开发了ARSENAL,一种新的DNA语言模型,以更好地识别调节性DNA动机并预测变异效应. 这种方法增强了对基因调节的理解,并有助于设计功能性DNA序列.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 在整个基因组上训练的自主监督的DNA语言模型 (DNALM) 与稀疏,异质的调节序列和短动机作斗争.
- 现有的注释无关的DNALM通常在监管任务上表现不佳,因为学习监管语法存在困难.
研究的目的:
- 介绍ARSENAL,这是一个针对调控序列分析进行优化的短文本掩盖DNA语言模型.
- 改善转录因子动机的发现和调控变异效应的预测.
- 增强监督学习任务,如染色体可访问性预测和监管变异得分.
主要方法:
- 在功能丰富的调节体上训练有素的ARSENAL,具有促进动机发现的新型调节剂.
- 评估了ARSENAL在零射击模式恢复和监管变体效应预测中的表现.
- 集成的ARSENAL嵌入到监督模型中,用于染色体可访问性预测和变异评分.
主要成果:
- 与其他DNALM相比,ARSENAL证明了转录因子动机的优异恢复 *de novo* 和更好的调节变异效应预测.
- 结合ARSENAL嵌入物显著提高了跨多种细胞类型的监督色素可访问性预测准确度.
- 阿森纳嵌入导致了加强监管变体评分,并使有针对性的监管序列设计成为可能.
结论:
- 阿森纳有效地解决了大规模DNALM在捕获调节序列特征方面的局限性.
- 该模型为动机发现,变异效应预测和功能基因组学分析提供了强大的工具.
- 阿森纳 (ARSENAL) 便于设计具有特定功能性质的调节序列.
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