Elsevier

Applied Soft Computing

Volume 96, November 2020, 106683
Applied Soft Computing

Analyzing COVID-19 virus based on enhanced fragmented biological Local Aligner using improved Ions Motion Optimization algorithm

https://doi.org/10.1016/j.asoc.2020.106683Get rights and content

Highlights

  • Find acceptable LCCS between COVID-19 and other biological sequences in short time.

  • Improving FLAT using modified Ions Motion Optimization (IMO).

  • Improving IMO algorithm based on merged it with PSO for enhancing its convergence.

  • Proposed model was tested using protein sequences from GenBank database of NCBI.

  • Developed model proves its high performance over with other Metaheuristic methods.

Abstract

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.

Keywords

COVID-19 virus
Ions motion optimization
Smith–Waterman alignment algorithm
Pairwise local alignment
Longest common consecutive substrings

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