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Analyzing SARS-CoV-2 Sequence Patterns by Semantic Trajectories.

Wissame Laddada1, Cecilia Zanni-Merk1, Lina F Soualmia1

  • 1Normandie Univ, UNIROUEN, INSAR, LITIS UR 4108, F-76000 Rouen, France.

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Summary
This summary is machine-generated.

This study introduces a novel graph-based model for analyzing viral genome sequences, enabling the identification of mutations in SARS-CoV-2 variants like Omicron. This approach aids in understanding virus evolution and tracking the spread of new strains.

Keywords:
CypherGraphNeo4jPattern analysisSARS-CoV-2

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Area of Science:

  • Genomics
  • Bioinformatics
  • Virology

Background:

  • The emergence of SARS-CoV-2 variants, such as Omicron, has led to rapid global spread and record contaminations.
  • Mutations in the virus's Spike region significantly alter its behavior and transmissibility.

Purpose of the Study:

  • To develop a method for analyzing viral genome sequences to understand virus evolution.
  • To extract and analyze sequence patterns in SARS-CoV-2 variants.

Main Methods:

  • A graph-based model was designed to represent viral genome organization.
  • Nodes represent nucleotides, and edges represent sequence connections, forming semantic trajectories.
  • Pattern extraction techniques were applied to the graph model.

Main Results:

  • The graph-based model successfully identified a mutated region in the Omicron variant (NCBI version: OM011974.1).
  • Semantic trajectories modeling proved effective for analyzing sequence patterns.

Conclusions:

  • The developed graph-based model offers a robust approach for analyzing viral genome evolution.
  • This method facilitates the identification of key mutations in emerging SARS-CoV-2 variants.