Classification of SARS-CoV-2 Variants through The Epistatical Circos Plots with Convolutional Neural Networks

Abstract

The COVID-19 pandemic has profoundly affected global health, driven by the remarkable transmissibility and mutational adaptability of the SARS-CoV-2 virus. Although five variants of concern, Alpha, Beta, Gamma, Delta, and Omicron, have been identified, the classification task in this study is formulated using four classes: Alpha, Delta, Omicron, and Else, reflecting the sequence availability and temporal coverage of the dataset. Here, we develop an integrative framework that combines direct coupling analysis (DCA), Circos-based visualization, and convolutional neural networks (CNNs) to characterize lineage-specific epistatic signatures from large-scale SARS-CoV-2 genomic sequences. DCA-inferred pairwise mutational couplings were transformed into Circos images, which were then used as inputs for CNN-based classification models. The proposed framework achieved robust variant classification, with the best-performing model reaching a weighted-average F1-score of $98.68\pm 0.75\%$ and an AUC close to 1.

References in corpus (17)

• Direct-coupling analysis of residue co-evolution captures native contacts across many protein families
• Identification of direct residue contacts in protein-protein interaction by message passing
• Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models
• Sequence co-evolution gives 3D contacts and structures of protein complexes
• High-dimensional Ising model selection using ${\ell }_1$-regularized logistic regression
• Inverse statistical problems: from the inverse Ising problem to data science
• Inverse Statistical Physics of Protein Sequences: A Key Issues Review
• Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences
• Inverse Ising inference using all the data
• Competition between recombination and epistasis can cause a transition from allele to genotype selection
• Statistical Genetics and Evolution of Quantitative Traits
• Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes
• Global analysis of more than 50,000 SARS-Cov-2 genomes reveals epistasis between 8 viral genes
• Emergence of clones in sexual populations
• Inferring genetic fitness from genomic data
• Statistical Genetics in and out of Quasi-Linkage Equilibrium (Extended)
• Two fitness inference schemes compared using allele frequencies from 1,068,391 sequences sampled in the UK during the COVID-19 pandemic