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一个深度神经网络用于预测和工程替代多化

Nicholas Bogard1, Johannes Linder2, Alexander B Rosenberg1

  • 1Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA.

Cell
|June 11, 2019
PubMed
概括

深度学习模型APARENT从DNA序列中预测了替代多基化 (APA). 该工具识别了调节APA的序列动机,并量化了基因变异对疾病的影响.

关键词:
在MPRA在SNV替代性多化关联调节深度学习生成模型mRNA处理机器学习大规模并行报告测试单核酸变体合成生物学

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科学领域:

  • 基因组学和生物信息学
  • 分子生物学
  • 计算生物学

背景情况:

  • 替代多基化 (APA) 对人类细胞中的转录组多样性有显著的贡献.
  • 了解APA的DNA序列决定因素对于解读基因调节至关重要.
  • 目前用于预测APA的方法通常需要实验数据或范围有限.

研究的目的:

  • 开发一种深度学习模型 (APARENT),能够仅从DNA序列中预测APA.
  • 确定管理APA的新序列动机和监管要素.
  • 将该模型应用于工程聚化信号,并评估基因变异对APA的影响.

主要方法:

  • 在300多万APA报告者及其表达数据的大数据集上训练了一种深度学习模型APARENT (APA回归Net).
  • 使用可视化技术来解释神经网络学习的序列特征.
  • 实验验证了该模型对工程聚化信号和遗传变异效应的预测.

主要成果:

  • 在合成和人类3'未翻译区域 (3'UTR) 中,APARENT在预测APA方面具有很高的准确性.
  • 该模型成功地确定了参与招募APA监管者的已知和新发现的序列动机.
  • APARENT有效量化了基因变异对APA的影响,识别了与各种疾病相关的致病变异.

结论:

  • 深度学习可以从DNA序列中准确预测替代多基化,为基因组分析提供了强大的工具.
  • 它揭示了控制3'端处理的复杂的cis-regulatory代码,并提供了对基因表达调节的见解.
  • 通过识别影响APA的致病变体,该模型对了解疾病的遗传基础具有重要意义.