Goal: Over the decades, there have been improvements in the sequence alignment algorithm, with significant advances in various aspects such as complexity and accuracy. However, human-defined algorithms have an explicit limitation in view of developmental completeness. This paper introduces a novel local alignment method to obtain optimal sequence alignment based on reinforcement learning. Methods: There is a DQNalign algorithm that learns and performs sequence alignment through deep reinforcement learning. This paper proposes a DQN x-drop algorithm that performs local alignment without human intervention by combining the x-drop algorithm with this DQNalign algorithm. The proposed algorithm performs local alignment by repeatedly observing the subsequences and selecting the next alignment direction until the x-drop algorithm terminates the DQNalign algorithm. This proposed algorithm has an advantage in view of linear computational complexity compared to conventional local alignment algorithms. Results: This paper compares alignment performance (coverage and identity) and complexity for a fair comparison between the proposed DQN x-drop algorithm and the conventional greedy x-drop algorithm. Firstly, we prove the proposed algorithm's superiority by comparing the two algorithms’ computational complexity through numerical analysis. After that, we tested the alignment performance actual HEV and E.coli sequence datasets. The proposed method shows the comparable identity and coverage performance to the conventional alignment method while having linear complexity for the $X$ parameter. Conclusions: Through this study, it was possible to confirm the possibility of a new local alignment algorithm that minimizes computational complexity without human intervention.

中文翻译：

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基于深度强化学习的DNA序列局部比对

目标：几十年来，序列比对算法得到了改进，在复杂性和准确性等各个方面都有了显着进步。然而，考虑到发展的完整性，人工定义的算法有一个明确的限制。本文介绍了一种新的基于强化学习的局部比对方法来获得最优序列比对。方法：有一种 DQNalign 算法通过深度强化学习来学习和执行序列比对。本文提出了一种 DQN x-drop 算法，通过将 x-drop 算法与 DQNalign 算法相结合，无需人工干预即可进行局部对齐。所提出的算法通过重复观察子序列并选择下一个对齐方向来执行局部对齐，直到 x-drop 算法终止 DQNalign 算法。与传统的局部对齐算法相比，该算法在线性计算复杂度方面具有优势。结果：本文比较了对齐性能（覆盖率和身份）和复杂性，以便在提出的 DQN x-drop 算法和传统的贪婪 x-drop 算法之间进行公平比较。首先，我们通过数值分析比较了两种算法的计算复杂度，证明了所提算法的优越性。之后，我们测试了实际 HEV 和大肠杆菌序列数据集的比对性能。所提出的方法显示了与传统对齐方法相当的身份和覆盖性能，同时具有线性复杂度$X$范围。结论：通过这项研究，可以确认一种新的局部对齐算法的可能性，该算法可以在没有人工干预的情况下最大限度地降低计算复杂性。