TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution

https://doi.org/10.1016/j.compbiomed.2022.106264Get rights and content

Highlights

  • We propose a transformer-based mutation prediction framework for SARS-CoV-2 evolution.

  • We design a phylogenetic tree-based sampling method to generate the viral sequences combined with temporal information.

  • TEMPO can predict mutations at sites that have not yet emerged, with 22 successfully predicted mutations since Feb 2022.

Abstract

The widespread of SARS-CoV-2 presents a significant threat to human society, as well as public health and economic development. Extensive efforts have been undertaken to battle against the pandemic, whereas effective approaches such as vaccination would be weakened by the continuous mutations, leading to considerable attention being attracted to the mutation prediction. However, most previous studies lack attention to phylogenetics. In this paper, we propose a novel and effective model TEMPO for predicting the mutation of SARS-CoV-2 evolution. Specifically, we design a phylogenetic tree-based sampling method to generate sequence evolution data. Then, a transformer-based model is presented for the site mutation prediction after learning the high-level representation of these sequence data. We conduct experiments to verify the effectiveness of TEMPO, leveraging a large-scale SARS-CoV- 2 dataset. Experimental results show that TEMPO is effective for mutation prediction of SARS- CoV-2 evolution and outperforms several state-of-the-art baseline methods. We further perform mutation prediction experiments of other infectious viruses, to explore the feasibility and robustness of TEMPO, and experimental results verify its superiority. The codes and datasets are freely available at https://github.com/ZJUDataIntelligence/TEMPO.

Keywords

SARS-CoV-2
Viral evolution
Natural language processing
Transformer-based method
Phylogenetic tree
Mutation prediction

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1

These authors have contributed equally to this work.

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