使用合成调节基因组学的深度学习模型的代改进
André M Ribeiro-Dos-Santos1,2, Matthew T Maurano3,4
1Institute for Systems Genetics, New York University Grossman School of Medicine, New York, New York 10016, USA.
Genome research
|October 22, 2025
概括
深度学习模型准确地预测表观遗传模式,但与新的DNA序列作斗争. 将这些模型与合成DNA数据微调,提高了它们用于变体分类的概括性.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.
背景情况:
- 深度学习模型可以从参考序列中预测全基因组的表观遗传模式.
- 这些模型对非参考序列 (如疾病变异) 的预测能力尚不清楚.
研究的目的:
- 评估Enformer模型在工程DNA序列上的性能,这些DNA序列与参考基因组有不同程度的分歧.
- 用合成调节基因组学数据提高深度学习模型用于表观遗传预测的概括性.
主要方法:
- 利用Enformer模型来预测跨工程序列的DNA可访问性和RNA转录.
- 采用合成调节基因组学来创建和测试数十个DNase I过敏位点 (DHS) 的删除,反转和重排.
- 微调了Enformer模型,使用来自工程序列的实验数据.
主要成果:
- 启动器在预测和实验DNA可访问性之间显示出良好的相关性,对于改变DHS顺序或方向的序列,性能下降.
- 对于与参考基因组非常相似的序列 (例如单个删除) 的模型性能是最好的.
- 微调显著减少了预测错误,并保持了其他表观遗传轨迹的强大预测性能.
结论:
- 当前的深度学习模型在预测具有关键特征分歧的新型序列的表观遗传模式方面存在局限性.
- 一种代方法,包括合成构造的分析,增强了模型的概括性.
- 这种改进的概括性对于在人口研究中发现的调节变异的功能分类至关重要.
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