Non-standard bioinformatics characterization of SARS-CoV-2

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Nucleotide sequence analysis is crucial for understanding viral evolution and origins.
• Existing bioinformatics methods may not fully capture complex genomic information.

Purpose of the Study:

• To introduce a novel bioinformatics method, 4D-Dynamic Representation of DNA/RNA Sequences, for analyzing nucleotide sequences.
• To apply this method to SARS-CoV-2 genome sequences and investigate potential origins.
• To demonstrate the method's applicability to other viral genomes, like Zika virus.

Main Methods:

• Representing DNA/RNA sequences as "material points" in 4D space, forming 4D-dynamic graphs.
• Treating these graphs as "rigid bodies" and characterizing them using classical dynamics principles (center of mass, moments of inertia).
• Projecting 4D graphs into 2D and 3D spaces to create classification maps.

Main Results:

• Generated 2D and 3D classification maps for SARS-CoV-2 genome sequences.
• Identified distinct clusters of points on the 3D maps.
• The distribution of clusters supports the hypothesis of SARS-CoV-2 originating in bats and pangolins.

Conclusions:

• The 4D-Dynamic Representation method provides a novel way to analyze and classify viral genome sequences.
• The findings support the zoonotic origin hypothesis for SARS-CoV-2.
• The method is versatile and applicable to studying the evolutionary dynamics of other viruses.