Progressive multiple sequence alignment for COVID-19 mutation identification via deep reinforcement learning

• Zanuba Hilla Qudrotu Chofsoh ^[1] ; Imam Mukhlash ^[1] ; Mohammad Iqbal ^[1] ; Bandung Arry Sanjoyo ^[1]
  1. [1] Department of Mathematics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
• Localización: Practical applications of computational biology and bioinformatics, 17th International Conference (PACBB 2023) / Miguel Rocha (ed. lit.), Florentino Fernández Riverola (ed. lit.), Mohd Saberi Mohamad (ed. lit.), Ana Belén Gil González (ed. lit.), 2023, ISBN 978-3-031-38078-5, págs. 73-83
• Idioma: inglés
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• Resumen

  • 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 10,8% around conserved region of nitrogenous bases.