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

TL;DR

This study introduces an innovative framework combining DCA, Circos visualization, and CNNs to classify SARS-CoV-2 variants with high accuracy using genomic sequence data.

Contribution

It presents a novel integrative approach that transforms mutational couplings into images for CNN-based variant classification, achieving high performance.

Findings

Achieved a weighted-average F1-score of 98.68%.

Developed a pipeline combining DCA, Circos plots, and CNNs.

Demonstrated robust classification of SARS-CoV-2 variants.

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.