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通过深度学习模型预测DNA存储中的编码序列的二次结构的程度

Wanmin Lin¹, Ling Chu¹, Xiangyu Yao¹

¹Institute of Computing Science and Technology, Guangzhou University, Guangzhou, Guangdong, China.

Scientific reports

|July 2, 2025

概括

DNA数据存储面临复杂的DNA二次结构带来的挑战. 一个新的BiLSTM-Transformer模型有效地预测和选高风险序列,提高DNA存储系统中的数据可靠性.

科学领域:

生物技术是生物技术.
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

DNA数据存储提供了巨大的容量,但受到DNA二次结构的阻碍.
这些结构使合成,放大和测序变得复杂,导致数据检索错误.
解决这些问题对于大规模的DNA存储应用至关重要.

研究的目的:

开发一种用于预测和过易形成有问题的二次结构的DNA序列的计算方法.
提高DNA数据存储系统中信息恢复的可靠性.

主要方法:

开发了一个BiLSTM-Transformer深度学习模型,结合了k-mer嵌入.
该模型预测了DNA序列的自由能量,这是二次结构形成的代理.
确定并选出具有高预测自由能量的序列.

主要成果:

与其他深度学习模型相比,具有k-mer嵌入的BiLSTM-Transformer模型表现出优异的预测性能.
对真实数据集的应用成功地确定了与读取错误增加和拷贝数量减少相关的高风险序列.
该模型有效地选出可能导致严重二次结构形成的序列.

结论:

拟议的BiLSTM-Transformer模型为在DNA数据存储中识别和减轻有问题的DNA二次结构提供了有效的解决方案.
这种主动选方法提高了从DNA存储系统中检索信息的可靠性和准确性.
这种方法代表了向强大的和可扩展的DNA数据存储解决方案迈出的重要一步.

关键词:

储存 DNA 储存 DNA 储存深度学习模型深度学习模型二级结构是次要结构.序列化是指测序的使用.综合合成综合合成

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