SARS-CoV-2 (COVID-19) virus is a havoc pandemic that infects millions of people over the world and thousands of infected cases dead. So, it is vital to propose new intelligent data analysis tools and enhance the existed ones to aid scientists in analyzing the COVID-19 virus. Fragmented Local Aligner Technique (FLAT) is a data analysis tool that is used for detecting the longest common consecutive subsequence (LCCS) between a pair of biological data sequences. FLAT is an aligner tool that can be used to find the LCCS between COVID-19 virus and other viruses to help in other biochemistry and biological operations. In this study, the enhancement of FLAT based on modified Ions Motion Optimization (IMO) is developed to produce acceptable LCCS with efficient performance in a reasonable time. The proposed method was tested to find the LCCS between Orflab poly-protein and surface glycoprotein of COVID-19 and other viruses. The experimental results demonstrate that the proposed model succeeded in producing the best LCCS against other algorithms using real LCCS measured by the SW algorithm as a reference.

SARS-CoV-2 (COVID-19) 病毒是一种严重的大流行病，它感染了全世界数百万人，数千名感染病例死亡。因此，提出新的智能数据分析工具并增强现有工具以帮助科学家分析 COVID-19 病毒至关重要。Fragmented Local Aligner Technique (FLAT) 是一种数据分析工具，用于检测一对生物数据序列之间的最长公共连续子序列 (LCCS)。FLAT 是一种对齐工具，可用于查找 COVID-19 病毒和其他病毒之间的 LCCS，以帮助其他生物化学和生物操作。在这项研究中，开发了基于改进离子运动优化 (IMO) 的 FLAT 增强，以在合理的时间内产生具有高效性能的可接受的 LCCS。对所提出的方法进行了测试，以发现 COVID-19 和其他病毒的 Orflab 多蛋白和表面糖蛋白之间的 LCCS。实验结果表明，使用 SW 算法测量的真实 LCCS 作为参考，所提出的模型成功地产生了相对于其他算法的最佳 LCCS。