转移学习揭示了对转录因子剂量的定量反应的序列决定因素
Sahin Naqvi1,2,3,4,5, Seungsoo Kim1,6,7, Saman Tabatabaee1,7
1Departments of Chemical and Systems Biology and Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA.
bioRxiv : the preprint server for biology
|June 10, 2024
概括
深度学习模型预测了转录因子 (TF) 度如何影响基因调节. 特定的DNA动图安排缓冲或使染色质可访问性对TF剂量变化的敏感,揭示了对cis调节代码的新见解.
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:
- 深度学习模型从转录因子 (TF) 结合基因预测细胞类型特定的染色质模式.
- 现有的模型缺乏对TF扰动的染色质反应的预测能力.
研究的目的:
- 开发和解释深度学习模型,预测对TF剂量反应的染色质可访问性变化.
- 了解TF结合基因特征如何影响面部原生细胞中的染色质可访问性.
主要方法:
- 利用转移学习来训练DNA序列数据上的深度学习模型.
- 在对TWIST1和SOX9.9的不同度的反应中,量化色素可访问性发生变化.
- 使用记者测定和生物物理模型验证的预测序列特征.
主要成果:
- 模型准确地预测了染色质可访问性变化,接近实验可重复性.
- 高亲和度,位于中心的图案与异型TF联合结合,缓冲TF剂量变化.
- 低亲和度或分散的同型基因导致了敏感的染色体可访问性反应.
结论:
- TF结合动机的特性在很大程度上决定了调节元件对TF剂量的敏感性.
- 缓冲和敏感特征是由进化净化选择形成的.
- 将深度学习与定量测量相结合,提供了一种强大的方法来破译cis-regulatory代码.
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