Accurate and timely monitoring of the evolution of SARS-CoV-2 is crucial for identifying and tracking potentially more transmissible/virulent viral variants, and implement mitigation strategies to limit their spread. Here we introduce HaploCoV, a novel software framework that enables the exploration of SARS-CoV-2 genomic diversity through space and time, to identify novel emerging viral variants and prioritize variants of potential epidemiological interest in a rapid and unsupervised manner. HaploCoV can integrate with any classification/nomenclature and incorporates an effective scoring system for the prioritization of SARS-CoV-2 variants. By performing retrospective analyses of more than 11.5 M genome sequences we show that HaploCoV demonstrates high levels of accuracy and reproducibility and identifies the large majority of epidemiologically relevant viral variants - as flagged by international health authorities - automatically and with rapid turn-around times.Our results highlight the importance of the application of strategies based on the systematic analysis and integration of regional data for rapid identification of novel, emerging variants of SARS-CoV-2. We believe that the approach outlined in this study will contribute to relevant advances to current and future genomic surveillance methods.

HaploCoV: unsupervised classification and rapid detection of novel emerging variants of SARS-CoV-2 / M. Chiara, D.S. Horner, E. Ferrandi, C. Gissi, G. Pesole. - In: COMMUNICATIONS BIOLOGY. - ISSN 2399-3642. - 6:1(2023 Apr 22), pp. 443.1-443.15. [10.1038/s42003-023-04784-4]

HaploCoV: unsupervised classification and rapid detection of novel emerging variants of SARS-CoV-2

M. Chiara
Primo
;
D.S. Horner
Secondo
;
E. Ferrandi;
2023

Abstract

Accurate and timely monitoring of the evolution of SARS-CoV-2 is crucial for identifying and tracking potentially more transmissible/virulent viral variants, and implement mitigation strategies to limit their spread. Here we introduce HaploCoV, a novel software framework that enables the exploration of SARS-CoV-2 genomic diversity through space and time, to identify novel emerging viral variants and prioritize variants of potential epidemiological interest in a rapid and unsupervised manner. HaploCoV can integrate with any classification/nomenclature and incorporates an effective scoring system for the prioritization of SARS-CoV-2 variants. By performing retrospective analyses of more than 11.5 M genome sequences we show that HaploCoV demonstrates high levels of accuracy and reproducibility and identifies the large majority of epidemiologically relevant viral variants - as flagged by international health authorities - automatically and with rapid turn-around times.Our results highlight the importance of the application of strategies based on the systematic analysis and integration of regional data for rapid identification of novel, emerging variants of SARS-CoV-2. We believe that the approach outlined in this study will contribute to relevant advances to current and future genomic surveillance methods.
Settore BIO/11 - Biologia Molecolare
   Connect and align ELIXIR Nodes to deliver sustainable FAIR life-science data management services (ELIXIR-CONVERGE)
   ELIXIR-CONVERGE
   EUROPEAN COMMISSION
   H2020
   871075
22-apr-2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/967644
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