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A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...

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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

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Published on: December 9, 2022

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

Binbin Zhou¹, Hang Zhou², Xue Zhang³

¹Department of Computer Science and Computing, Zhejiang University City College, No. 48 Huzhou Street, Hangzhou, 310015, China; Industry Brain Institute, Zhejiang University City College, Hangzhou, 310015, China.

Computers in Biology and Medicine

|December 19, 2022

Summary

This summary is machine-generated.

Predicting SARS-CoV-2 mutations is crucial as variants challenge vaccines. TEMPO, a novel phylogenetic and transformer-based model, accurately forecasts viral evolution and outperforms existing methods.

Keywords:

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

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Published on: December 9, 2015

Area of Science:

Virology
Computational Biology
Genomics

Background:

The continuous mutation of SARS-CoV-2 poses a significant threat to public health and economic stability.
Vaccine efficacy is challenged by viral evolution, necessitating accurate mutation prediction.
Previous studies often overlook the importance of phylogenetics in mutation prediction.

Purpose of the Study:

To propose and validate TEMPO, a novel model for predicting SARS-CoV-2 evolution and mutations.
To incorporate phylogenetic insights into a machine learning framework for enhanced prediction accuracy.
To assess the generalizability and robustness of TEMPO across different infectious viruses.

Main Methods:

Development of a phylogenetic tree-based sampling method for generating sequence evolution data.
Implementation of a transformer-based model to learn high-level representations of viral sequence data.
Leveraging a large-scale SARS-CoV-2 dataset for model training and validation.

Main Results:

TEMPO demonstrates high effectiveness in predicting SARS-CoV-2 mutations.
The proposed model significantly outperforms several state-of-the-art baseline methods.
Experiments on other infectious viruses confirm TEMPO's feasibility and robustness.

Conclusions:

TEMPO offers a powerful and effective approach for predicting viral mutations, particularly for SARS-CoV-2.
The integration of phylogenetics enhances the accuracy and reliability of mutation prediction models.
TEMPO shows promise for broader applications in tracking and predicting the evolution of infectious diseases.