Progressive Multiple Sequence Alignment for COVID-19 Mutation Identification via Deep Reinforcement Learning

Zanuba Hilla Qudrotu Chofsoh; Imam Mukhlash; Mohammad Iqbal; Bandung Arry Sanjoyo

doi:10.1007/978-3-031-38079-2_8

Progressive Multiple Sequence Alignment for COVID-19 Mutation Identification via Deep Reinforcement Learning

Zanuba Hilla Qudrotu Chofsoh, Imam Mukhlash^*, Mohammad Iqbal, Bandung Arry Sanjoyo

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

COVID-19 can mutate rapidly, resulting in new variants which could be more malignant. To recognize the new variant, we must identify the mutation parts by locating the nucleotide changes in the DNA sequence of COVID-19. The identification is by processing sequence alignment. In this work, we propose a method to perform multiple sequence alignment via deep reinforcement learning effectively. The proposed method integrates a progressive alignment approach by aligning each pairwise sequence center to deep Q networks. We designed the experiment by evaluating the proposed method on five COVID-19 variants: alpha, beta, delta, gamma, and omicron. The experiment results showed that the proposed method was successfully applied to align multiple COVID-19 DNA sequences by demonstrating that pairwise alignment processes can precisely locate the sequence mutation up to 90 %. Moreover, we effectively identify the mutation in multiple sequence alignments fashion by discovering around 10.8 % conserved region of nitrogenous bases.

Original language	English
Title of host publication	Practical Applications of Computational Biology and Bioinformatics, 17th International Conference (PACBB 2023)
Editors	Miguel Rocha, Florentino Fdez-Riverola, Mohd Saberi Mohamad, Ana Belén Gil-González
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	73-83
Number of pages	11
ISBN (Print)	9783031380785
DOIs	https://doi.org/10.1007/978-3-031-38079-2_8
Publication status	Published - 2023
Event	17th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2023 - Guimaraes, Portugal Duration: 12 Jul 2023 → 14 Jul 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	743 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	17th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2023
Country/Territory	Portugal
City	Guimaraes
Period	12/07/23 → 14/07/23

Keywords

COVID-19
DNA Sequence
Deep Reinforcement Learning
Multiple Sequence Alignment
Mutation

Access to Document

10.1007/978-3-031-38079-2_8

Cite this

Chofsoh, Z. H. Q., Mukhlash, I., Iqbal, M., & Sanjoyo, B. A. (2023). Progressive Multiple Sequence Alignment for COVID-19 Mutation Identification via Deep Reinforcement Learning. In M. Rocha, F. Fdez-Riverola, M. S. Mohamad, & A. B. Gil-González (Eds.), Practical Applications of Computational Biology and Bioinformatics, 17th International Conference (PACBB 2023) (pp. 73-83). (Lecture Notes in Networks and Systems; Vol. 743 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-38079-2_8

Chofsoh, Zanuba Hilla Qudrotu ; Mukhlash, Imam ; Iqbal, Mohammad et al. / Progressive Multiple Sequence Alignment for COVID-19 Mutation Identification via Deep Reinforcement Learning. Practical Applications of Computational Biology and Bioinformatics, 17th International Conference (PACBB 2023). editor / Miguel Rocha ; Florentino Fdez-Riverola ; Mohd Saberi Mohamad ; Ana Belén Gil-González. Springer Science and Business Media Deutschland GmbH, 2023. pp. 73-83 (Lecture Notes in Networks and Systems).

@inproceedings{f199f459bea048f7994fc4aa283d3638,

title = "Progressive Multiple Sequence Alignment for COVID-19 Mutation Identification via Deep Reinforcement Learning",

abstract = "COVID-19 can mutate rapidly, resulting in new variants which could be more malignant. To recognize the new variant, we must identify the mutation parts by locating the nucleotide changes in the DNA sequence of COVID-19. The identification is by processing sequence alignment. In this work, we propose a method to perform multiple sequence alignment via deep reinforcement learning effectively. The proposed method integrates a progressive alignment approach by aligning each pairwise sequence center to deep Q networks. We designed the experiment by evaluating the proposed method on five COVID-19 variants: alpha, beta, delta, gamma, and omicron. The experiment results showed that the proposed method was successfully applied to align multiple COVID-19 DNA sequences by demonstrating that pairwise alignment processes can precisely locate the sequence mutation up to 90 %. Moreover, we effectively identify the mutation in multiple sequence alignments fashion by discovering around 10.8 % conserved region of nitrogenous bases.",

keywords = "COVID-19, DNA Sequence, Deep Reinforcement Learning, Multiple Sequence Alignment, Mutation",

author = "Chofsoh, {Zanuba Hilla Qudrotu} and Imam Mukhlash and Mohammad Iqbal and Sanjoyo, {Bandung Arry}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2023 ; Conference date: 12-07-2023 Through 14-07-2023",

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Chofsoh, ZHQ, Mukhlash, I , Iqbal, M & Sanjoyo, BA 2023, Progressive Multiple Sequence Alignment for COVID-19 Mutation Identification via Deep Reinforcement Learning. in M Rocha, F Fdez-Riverola, MS Mohamad & AB Gil-González (eds), Practical Applications of Computational Biology and Bioinformatics, 17th International Conference (PACBB 2023). Lecture Notes in Networks and Systems, vol. 743 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 73-83, 17th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2023, Guimaraes, Portugal, 12/07/23. https://doi.org/10.1007/978-3-031-38079-2_8

Progressive Multiple Sequence Alignment for COVID-19 Mutation Identification via Deep Reinforcement Learning. / Chofsoh, Zanuba Hilla Qudrotu; Mukhlash, Imam ; Iqbal, Mohammad et al.
Practical Applications of Computational Biology and Bioinformatics, 17th International Conference (PACBB 2023). ed. / Miguel Rocha; Florentino Fdez-Riverola; Mohd Saberi Mohamad; Ana Belén Gil-González. Springer Science and Business Media Deutschland GmbH, 2023. p. 73-83 (Lecture Notes in Networks and Systems; Vol. 743 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Progressive Multiple Sequence Alignment for COVID-19 Mutation Identification via Deep Reinforcement Learning

AU - Chofsoh, Zanuba Hilla Qudrotu

AU - Mukhlash, Imam

AU - Iqbal, Mohammad

AU - Sanjoyo, Bandung Arry

PY - 2023

Y1 - 2023

N2 - COVID-19 can mutate rapidly, resulting in new variants which could be more malignant. To recognize the new variant, we must identify the mutation parts by locating the nucleotide changes in the DNA sequence of COVID-19. The identification is by processing sequence alignment. In this work, we propose a method to perform multiple sequence alignment via deep reinforcement learning effectively. The proposed method integrates a progressive alignment approach by aligning each pairwise sequence center to deep Q networks. We designed the experiment by evaluating the proposed method on five COVID-19 variants: alpha, beta, delta, gamma, and omicron. The experiment results showed that the proposed method was successfully applied to align multiple COVID-19 DNA sequences by demonstrating that pairwise alignment processes can precisely locate the sequence mutation up to 90 %. Moreover, we effectively identify the mutation in multiple sequence alignments fashion by discovering around 10.8 % conserved region of nitrogenous bases.

AB - COVID-19 can mutate rapidly, resulting in new variants which could be more malignant. To recognize the new variant, we must identify the mutation parts by locating the nucleotide changes in the DNA sequence of COVID-19. The identification is by processing sequence alignment. In this work, we propose a method to perform multiple sequence alignment via deep reinforcement learning effectively. The proposed method integrates a progressive alignment approach by aligning each pairwise sequence center to deep Q networks. We designed the experiment by evaluating the proposed method on five COVID-19 variants: alpha, beta, delta, gamma, and omicron. The experiment results showed that the proposed method was successfully applied to align multiple COVID-19 DNA sequences by demonstrating that pairwise alignment processes can precisely locate the sequence mutation up to 90 %. Moreover, we effectively identify the mutation in multiple sequence alignments fashion by discovering around 10.8 % conserved region of nitrogenous bases.

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KW - Multiple Sequence Alignment

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Y2 - 12 July 2023 through 14 July 2023

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Chofsoh ZHQ, Mukhlash I , Iqbal M , Sanjoyo BA. Progressive Multiple Sequence Alignment for COVID-19 Mutation Identification via Deep Reinforcement Learning. In Rocha M, Fdez-Riverola F, Mohamad MS, Gil-González AB, editors, Practical Applications of Computational Biology and Bioinformatics, 17th International Conference (PACBB 2023). Springer Science and Business Media Deutschland GmbH. 2023. p. 73-83. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-38079-2_8