Pairwise Heuristic Sequence Alignment Algorithm Based on Deep Reinforcement Learning

<italic>Goal:</italic> 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 seq...

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Autores principales:	Yong-Joon Song, Dong Jin Ji, Hyein Seo, Gyu-Bum Han, Dong-Ho Cho
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Deep reinforcement learning global alignment pairwise alignment sequence alignment sequence comparison Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/5d1a7f2f387943dbafa35a048b3decd4
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id	oai:doaj.org-article:5d1a7f2f387943dbafa35a048b3decd4
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spelling	oai:doaj.org-article:5d1a7f2f387943dbafa35a048b3decd42021-11-26T00:02:04ZPairwise Heuristic Sequence Alignment Algorithm Based on Deep Reinforcement Learning2644-127610.1109/OJEMB.2021.3055424https://doaj.org/article/5d1a7f2f387943dbafa35a048b3decd42021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9340257/https://doaj.org/toc/2644-1276<italic>Goal:</italic> 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. <italic>Methods:</italic> 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. <italic>Results:</italic> 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. <italic>Conclusions:</italic> 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.Yong-Joon SongDong Jin JiHyein SeoGyu-Bum HanDong-Ho ChoIEEEarticleDeep reinforcement learningglobal alignmentpairwise alignmentsequence alignmentsequence comparisonComputer applications to medicine. Medical informaticsR858-859.7Medical technologyR855-855.5ENIEEE Open Journal of Engineering in Medicine and Biology, Vol 2, Pp 36-43 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep reinforcement learning global alignment pairwise alignment sequence alignment sequence comparison Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5
spellingShingle	Deep reinforcement learning global alignment pairwise alignment sequence alignment sequence comparison Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5 Yong-Joon Song Dong Jin Ji Hyein Seo Gyu-Bum Han Dong-Ho Cho Pairwise Heuristic Sequence Alignment Algorithm Based on Deep Reinforcement Learning
description	<italic>Goal:</italic> 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. <italic>Methods:</italic> 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. <italic>Results:</italic> 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. <italic>Conclusions:</italic> 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.
format	article
author	Yong-Joon Song Dong Jin Ji Hyein Seo Gyu-Bum Han Dong-Ho Cho
author_facet	Yong-Joon Song Dong Jin Ji Hyein Seo Gyu-Bum Han Dong-Ho Cho
author_sort	Yong-Joon Song
title	Pairwise Heuristic Sequence Alignment Algorithm Based on Deep Reinforcement Learning
title_short	Pairwise Heuristic Sequence Alignment Algorithm Based on Deep Reinforcement Learning
title_full	Pairwise Heuristic Sequence Alignment Algorithm Based on Deep Reinforcement Learning
title_fullStr	Pairwise Heuristic Sequence Alignment Algorithm Based on Deep Reinforcement Learning
title_full_unstemmed	Pairwise Heuristic Sequence Alignment Algorithm Based on Deep Reinforcement Learning
title_sort	pairwise heuristic sequence alignment algorithm based on deep reinforcement learning
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/5d1a7f2f387943dbafa35a048b3decd4
work_keys_str_mv	AT yongjoonsong pairwiseheuristicsequencealignmentalgorithmbasedondeepreinforcementlearning AT dongjinji pairwiseheuristicsequencealignmentalgorithmbasedondeepreinforcementlearning AT hyeinseo pairwiseheuristicsequencealignmentalgorithmbasedondeepreinforcementlearning AT gyubumhan pairwiseheuristicsequencealignmentalgorithmbasedondeepreinforcementlearning AT donghocho pairwiseheuristicsequencealignmentalgorithmbasedondeepreinforcementlearning
_version_	1718409970526978048

Pairwise Heuristic Sequence Alignment Algorithm Based on Deep Reinforcement Learning

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