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Local Alignment of DNA Sequence Based on Deep Reinforcement Learning
1School of Electrical EngineeringKorea Advanced Institute of Science and Technology Daejeon 305-701 South Korea.
IEEE Open Journal of Engineering in Medicine and Biology
|April 11, 2022
Summary
This study introduces a novel deep reinforcement learning algorithm for sequence alignment, achieving comparable performance to traditional methods with reduced computational complexity. The new DQN x-drop algorithm minimizes complexity without human intervention.
Area of Science:
- Bioinformatics
- Computational Biology
- Machine Learning
Background:
- Traditional sequence alignment algorithms face limitations in developmental completeness.
- Advances in algorithms have improved complexity and accuracy over decades.
Purpose of the Study:
- Introduce a novel local alignment method using reinforcement learning.
- Develop an automated sequence alignment approach minimizing human intervention.
Main Methods:
- Proposed the DQN x-drop algorithm, combining DQNalign with the x-drop algorithm.
- Utilized deep reinforcement learning for sequence alignment through subsequence observation and direction selection.
- Algorithm terminates via the x-drop condition.
Main Results:
- Demonstrated linear computational complexity compared to conventional algorithms.
- Achieved comparable identity and coverage performance on HEV and E.coli datasets.
- Numerical analysis confirmed superiority in computational complexity.
Conclusions:
- Confirmed the feasibility of a new local alignment algorithm.
- The algorithm minimizes computational complexity without human intervention.
- Offers a promising alternative for sequence alignment tasks.
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