Identification of mathematical patterns in genomic spectrograms linked to variant classification in complete
Ana Guerrero-Tamayo1, Borja Sanz Urquijo2, María-Dolores Moragues Tosantos3
1Faculty of Engineering, University of Deusto, 48007, Bilbao, Biscay, Spain. ana.guerrero@deusto.es.
Scientific Reports
|December 5, 2025
Summary
Mathematical patterns in viral genomes, like SARS-CoV-2, can define characteristics. This study used genomic spectrograms and transfer learning to classify variants, revealing key patterns for identification.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Previous studies identified mathematical patterns in viral genomes.
- These patterns may determine viral characteristics.
- Hypothesis: Inherent genomic mathematical patterns dictate viral features.
Purpose of the Study:
- Explore mathematical patterns in SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) variant classification.
- Develop a methodology for variant identification using genomic data.
- Investigate the role of specific nucleotide frequencies in variant differentiation.
Main Methods:
- Genomic spectrograms generation from viral sequences.
- Two-stage transfer learning approach using pre-trained Convolutional Neural Networks (CNNs).
- Two-step explainability for identifying significant genomic regions and patterns.
Main Results:
- Identified distinct mathematical patterns characterizing specific SARS-CoV-2 variants.
- Highlighted the genomic region from the S gene to 3'UTR as crucial for variant identification.
- Nucleotide frequencies, particularly G and C, were key identifiers, with shared patterns in Omicron and pre-VOC lineages.
Conclusions:
- Mathematical patterns are significantly associated with SARS-CoV-2 variant classification.
- These patterns represent an additional layer of genomic information for efficient virus characterization.
- Findings suggest potential phylogenetic connections or evolutionary pathways within SARS-CoV-2 lineages.
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