Goal: Various methods have been developed to analyze the association between organisms and their genomic sequences. Among them, sequence alignment is the most frequently used method for comparative analysis of biological genomes. We intend to propose a novel pairwise sequence alignment method using deep reinforcement learning to break out the old pairwise alignment algorithms. Methods: We defined the environment and agent to enable reinforcement learning in the sequence alignment system. This novel method, named DQNalign, can immediately determine the next direction by observing the subsequences within the moving window. Results: DQNalign shows superiority in the dissimilar sequence pairs that have low identity values. And theoretically, we confirm that DQNalign has a low dimension for the sequence length in view of the complexity. Conclusions: This research shows the application method of deep reinforcement learning to the sequence alignment system and how deep reinforcement learning can improve the conventional sequence alignment method.

中文翻译：

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基于深度强化学习的成对启发式序列对齐算法

目标：已经开发了各种方法来分析生物体与其基因组序列之间的关联。其中，序列比对是生物基因组比较分析中最常用的方法。我们打算提出一种新的成对序列比对方法，使用深度强化学习来打破旧的成对比对算法。方法：我们定义了环境和代理以在序列比对系统中启用强化学习。这种名为 DQNalign 的新方法可以通过观察移动窗口内的子序列来立即确定下一个方向。结果：DQNalign 在具有低同一性值的不同序列对中显示出优势。从理论上讲，鉴于复杂性，我们确认 DQNalign 的序列长度维度较低。结论：本研究展示了深度强化学习在序列比对系统中的应用方法，以及深度强化学习如何改进传统的序列比对方法。