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.
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Acknowledgment
The authors gratefully acknowledge financial support from the Institut Teknologi Sepuluh Nopember for this work, under project scheme of the Publication Writing and IPR Incentive Program (PPHKI) 2023.
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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: Rocha, M., Fdez-Riverola, F., Mohamad, M.S., Gil-González, A.B. (eds) Practical Applications of Computational Biology and Bioinformatics, 17th International Conference (PACBB 2023). PACBB 2023. Lecture Notes in Networks and Systems, vol 743. Springer, Cham. https://doi.org/10.1007/978-3-031-38079-2_8
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DOI: https://doi.org/10.1007/978-3-031-38079-2_8
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