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, Seungsoo Kim2, Saman Tabatabaee3
1Departments of Chemical and Systems Biology and Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA; Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.
Cell genomics
|February 28, 2025
概括
深度学习可以预测转录因子 (TF) 剂量如何影响染色质可访问性. 特定的TF结合动机特征缓冲或使细胞对TF变化敏感,揭示新的监管代码层.
科学领域:
- 基因组学就是基因组学.
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 深度学习模型擅长从转录因子 (TF) 结合基因预测细胞类型特定的染色质模式.
- 然而,它们在乱的生物环境中的应用是有限的.
研究的目的:
- 应用转移学习来预测TF度如何影响面部原生细胞中的染色质可访问性.
- 研究TF结合动机特征在缓冲或敏感调控元件 (RE) 对于TF剂量变化的作用.
主要方法:
- 利用转移学习来建模TWIST1和SOX9TF度对染色质可访问性的影响.
- 分析了高和低亲和度的TF结合动机,考虑异型/同型的共同结合和在RE中的定位.
- 雇佣报告员测试以验证序列特征和TF-核酶体竞争模型.
主要成果:
- 在预测因TF剂量而导致的染色质可访问性变化方面取得了接近实验的准确性.
- 确定了高亲和度,位于中心的图案,作为缓冲特征,防止TF剂量波动.
- 发现低亲和度或分散的图案作为敏感特征,驱动对最小TF变化的反应.
- 观察到对缓冲和敏感化图案特征的净化选择特征.
- 证明了TF-核酶体竞争作为低亲和度动机敏感性的机制.
结论:
- 转移学习与定量色素反应测量相结合,为破译cis调节代码提供了一种新的方法.
- 特定的TF结合动机特性决定了细胞对TF剂量变化的反应,影响了基因调节.
- 了解这些序列特征可以了解监管元素的进化约束.
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