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HaploCoV: unsupervised classification and rapid detection of novel emerging variants of SARS-CoV-2.
Chiara, Matteo; Horner, David S; Ferrandi, Erika; Gissi, Carmela; Pesole, Graziano.
  • Chiara M; Department of Biosciences, University of Milan, Milan, Italy. matteo.chiara@unimi.it.
  • Horner DS; Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Consiglio Nazionale delle Ricerche, Bari, Italy. matteo.chiara@unimi.it.
  • Ferrandi E; Department of Biosciences, University of Milan, Milan, Italy.
  • Gissi C; Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Consiglio Nazionale delle Ricerche, Bari, Italy.
  • Pesole G; Department of Biosciences, University of Milan, Milan, Italy.
Commun Biol ; 6(1): 443, 2023 04 22.
Article in English | MEDLINE | ID: covidwho-2293364
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Systematic review/Meta Analysis Topics: Variants Limits: Humans Language: English Journal: Commun Biol Year: 2023 Document Type: Article Affiliation country: S42003-023-04784-4

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Systematic review/Meta Analysis Topics: Variants Limits: Humans Language: English Journal: Commun Biol Year: 2023 Document Type: Article Affiliation country: S42003-023-04784-4