Local 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 l...

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Bibliographic Details
Published in:IEEE open journal of engineering in medicine and biology Vol. 2; pp. 170 - 178
Main Authors: Song, Yong-Joon, Cho, Dong-Ho
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Alignment
Biomedical engineering
Complexity
Computer applications
Deep learning
Deep reinforcement learning
E coli
Genomics
Greedy algorithms
Heuristic algorithms
local alignment
Machine learning
meta learning
Neural networks
Nucleotide sequence
Numerical analysis
Reinforcement
Reinforcement learning
sequence alignment
sequence comparison
Training
local alignment
Deep reinforcement learning
sequence alignment
sequence comparison
meta learning
ISSN:2644-1276, 2644-1276
Online Access:Get full text
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Summary: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 <inline-formula><tex-math notation="LaTeX">X</tex-math></inline-formula> 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.
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ISSN:2644-1276
2644-1276
DOI:10.1109/OJEMB.2021.3076156