通过深度学习方法在DNA存储中具有高度二次结构的极限和屏幕序列
Wanmin Lin1, Ling Chu1, Yanqing Su1
1Institute of Computing Science and Technology, Guangzhou University, Guangzhou, Guangdong, China.
Computers in biology and medicine
|October 6, 2023
概括
控制DNA二次结构是准确DNA数据存储的关键. 一个深度学习模型预测了DNA的自由能量,使编码长度的选择 (例如100nt) 能够最大限度地减少有问题的结构.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 单链DNA/RNA中的二次结构很常见,特别是在长序列中.
- 高度的二次结构可以破坏DNA数据存储中的信息完整性.
- 减轻DNA二次结构副作用的策略尚未得到充分研究.
研究的目的:
- 为了研究DNA序列长度和二次结构形成之间的关系.
- 开发一个DNA序列自由能量预测模型.
- 建立用于数据存储的DNA序列中控制二次结构形成的方法.
主要方法:
- 在随机生成的不同长度的DNA序列中分析自由能量分布.
- 开发一个双向长期短期记忆 (BiLSTM) -注意力深度学习模型,用于免费能源预测.
- 使用诸如平均相对误差 (MRE) 和确定系数 (R2) 等指标验证模型的性能.
主要成果:
- DNA 序列自由能量遵循右倾分布,平均值随序列长度增加而增加.
- 100个核酸 (nt) 的编码长度可以通过保持低于20kcal/mol的自由能量值,将严重的二次结构限制在1%以内.
- BiLSTM注意力模型实现了高预测准确性 (MRE = 0.109,R2 = 0.918),超过了传统模型.
- 该模型展示了线性时间复杂性,适合大规模应用.
结论:
- 开发的深度学习模型有效地预测DNA序列的自由能量,并识别容易发生严重二次结构的序列.
- 选择适当的编码长度对于最小化DNA数据存储中的二次结构干扰至关重要.
- 该模型显示了在现实数据集中选有问题的序列的前景,提高了数据可靠性和测序效率.
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