通过学习紫外线损伤指纹来绘制转录因子结合点的地图
Hannah E Wilson1, Scott Stevison1, Levi Lamprey1
1School of Molecular Biosciences, Washington State University, Pullman, WA 99164, USA.
Nucleic acids research
|October 16, 2025
概括
新的机器学习方法利用紫外线诱导的DNA损伤模式,称为环丁二胺二聚体 (CPD) 指纹,在基因组中精确地绘制序列特定的转录因子结合部位 (TFBS).
科学领域:
- 基因组学就是基因组学.
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 对特定序列转录因子结合位点 (TFBS) 的准确映射对于理解转录网络至关重要.
- 目前的方法,如染色体免疫沉 (ChIP),在识别所有TFBS方面存在局限性.
研究的目的:
- 开发和验证一种使用机器学习和UV诱导的DNA损伤模式识别TFBS的新方法.
- 证明CPD-seq数据对于发现传统技术无法发现的新TFBS的实用性.
主要方法:
- 使用机器学习算法 (随机森林,神经网络) 分析循环butan胺二聚体 (CPD) 测序 (CPD-seq) 数据.
- 对酵母中的特定转录因子 (Hap2/Hap3/Hap5复合体,Gcr1) 和人类细胞中的核因子-Y的TFBS的鉴定.
- 通过与相关基因功能和mRNA表达水平的关联来验证新发现的TFBS.
主要成果:
- 鉴定了75个酵母中Hap2/Hap3/Hap5复合物的TFBS,包括25个新网站.
- 在酵母中发现了63个Gcr1TFBS,使用在有限的已知网站上训练的神经网络.
- 在人类细胞中发现了核因子-Y复合体的新结合点.
- 新发现的TFBS与预期的生物功能和突变菌株的改变基因表达有关.
结论:
- 通过机器学习,可以识别TFBS中独特的紫外线损伤模式 (CPD指纹).
- 这种方法为绘制TFBS提供了更高的精度和单核酸分辨率.
- 该方法对监管元素的综合性全基因组识别具有前景.
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