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在基因组尺度上评估基底分辨率DNA机制
Wen-Jie Jiang1,2, Congcong Hu3, Futing Lai2
1Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, 100142 Beijing, China.
Nucleic acids research
|September 12, 2023
概括
我们开发了BendNet,这是一个深度学习工具,可以在基本分辨率下预测DNA曲性. 这种方法有助于理解DNA机制及其在物种间生物过程中的作用.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 分子生物学分子生物学
背景情况:
- 内在的DNA特性,如曲,对于生物功能至关重要.
- 高通量循环序列技术能够评估DNA曲性,但对于大型基因组来说,这是一个挑战.
- 基准分辨率DNA曲性预测对于理解基因组调节至关重要.
研究的目的:
- 介绍BendNet,这是一个用于预测基本分辨率DNA可曲性的深度神经网络.
- 为了训练BendNet使用酵母的循环序列数据.
- 将BendNet应用于大规模的基因组分析,并创建一个全物种的可曲性资源.
主要方法:
- 开发了BendNet,一个深度神经网络模型.
- 利用酵母的循环序列实验数据用于模型训练.
- 应用BendNet来预测人类基因组和307种其他物种的DNA曲性.
主要成果:
- BendNet在基本分辨率上准确预测DNA曲性.
- 人类基因组中预测的DNA曲性与染色质特征和与疾病相关的区域相关.
- 确定了DNA可曲性和转录调节,DNA复制,转录因子结合和异染色体循环DNA生成之间的关联.
结论:
- BendNet是一个强大的工具,用于预测内在DNA曲.
- DNA 曲性影响了哺乳动物的关键基因组过程和疾病风险.
- 现在可以获得307个物种的基因组DNA曲性概况的综合资源,以及用于序列分析的在线工具.
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