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Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants

Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic composition-based machine learning methods have recently performed well in identifying phenotype–genotype relationships. We introduced a framework involving...

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Autores principales: Li, Jing, Wu, Ya-Nan, Zhang, Sen, Kang, Xiao-Ping, Jiang, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9116219/
https://www.ncbi.nlm.nih.gov/pubmed/35233612
http://dx.doi.org/10.1093/bib/bbac036
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author Li, Jing
Wu, Ya-Nan
Zhang, Sen
Kang, Xiao-Ping
Jiang, Tao
author_facet Li, Jing
Wu, Ya-Nan
Zhang, Sen
Kang, Xiao-Ping
Jiang, Tao
author_sort Li, Jing
collection PubMed
description Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic composition-based machine learning methods have recently performed well in identifying phenotype–genotype relationships. We introduced a framework involving dinucleotide (DNT) composition representation (DCR) to parse the general human adaptation of RNA viruses and applied a three-dimensional convolutional neural network (3D CNN) analysis to learn the human adaptation of other existing coronaviruses (CoVs) and predict the adaptation of SARS-CoV-2 variants of concern (VOCs). A markedly separable, linear DCR distribution was observed in two major genes—receptor-binding glycoprotein and RNA-dependent RNA polymerase (RdRp)—of six families of single-stranded (ssRNA) viruses. Additionally, there was a general host-specific distribution of both the spike proteins and RdRps of CoVs. The 3D CNN based on spike DCR predicted a dominant type II adaptation of most Beta, Delta and Omicron VOCs, with high transmissibility and low pathogenicity. Type I adaptation with opposite transmissibility and pathogenicity was predicted for SARS-CoV-2 Alpha VOCs (77%) and Kappa variants of interest (58%). The identified adaptive determinants included D1118H and A570D mutations and local DNTs. Thus, the 3D CNN model based on DCR features predicts SARS-CoV-2, a major type II human adaptation and is qualified to predict variant adaptation in real time, facilitating the risk-assessment of emerging SARS-CoV-2 variants and COVID-19 control.
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spelling pubmed-91162192022-05-19 Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants Li, Jing Wu, Ya-Nan Zhang, Sen Kang, Xiao-Ping Jiang, Tao Brief Bioinform Problem Solving Protocol Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic composition-based machine learning methods have recently performed well in identifying phenotype–genotype relationships. We introduced a framework involving dinucleotide (DNT) composition representation (DCR) to parse the general human adaptation of RNA viruses and applied a three-dimensional convolutional neural network (3D CNN) analysis to learn the human adaptation of other existing coronaviruses (CoVs) and predict the adaptation of SARS-CoV-2 variants of concern (VOCs). A markedly separable, linear DCR distribution was observed in two major genes—receptor-binding glycoprotein and RNA-dependent RNA polymerase (RdRp)—of six families of single-stranded (ssRNA) viruses. Additionally, there was a general host-specific distribution of both the spike proteins and RdRps of CoVs. The 3D CNN based on spike DCR predicted a dominant type II adaptation of most Beta, Delta and Omicron VOCs, with high transmissibility and low pathogenicity. Type I adaptation with opposite transmissibility and pathogenicity was predicted for SARS-CoV-2 Alpha VOCs (77%) and Kappa variants of interest (58%). The identified adaptive determinants included D1118H and A570D mutations and local DNTs. Thus, the 3D CNN model based on DCR features predicts SARS-CoV-2, a major type II human adaptation and is qualified to predict variant adaptation in real time, facilitating the risk-assessment of emerging SARS-CoV-2 variants and COVID-19 control. Oxford University Press 2022-03-02 /pmc/articles/PMC9116219/ /pubmed/35233612 http://dx.doi.org/10.1093/bib/bbac036 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Problem Solving Protocol
Li, Jing
Wu, Ya-Nan
Zhang, Sen
Kang, Xiao-Ping
Jiang, Tao
Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants
title Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants
title_full Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants
title_fullStr Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants
title_full_unstemmed Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants
title_short Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants
title_sort deep learning based on biologically interpretable genome representation predicts two types of human adaptation of sars-cov-2 variants
topic Problem Solving Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9116219/
https://www.ncbi.nlm.nih.gov/pubmed/35233612
http://dx.doi.org/10.1093/bib/bbac036
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