biostudies-literatureUnknown2Song YJGoal: 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 nexIEEE open journal of engineering in medicine and biology170-178https://www.ebi.ac.uk/biostudies/studies/S-EPMC8975175biostudies-literatureLocal Alignment of DNA Sequence Based on Deep Reinforcement Learning.PMC8975175Song YJCho DHfalseLocal Alignment of DNA Sequence Based on Deep Reinforcement Learning.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 nex2021-01-01T00:00:00Z20212025-04-19T07:48:20.138Z2025-04-19T07:48:20.138ZS-EPMC89751753540298210.1109/OJEMB.2021.3076156