In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 variants of interest
Abstract Rapidly evolving viral pathogens such as SARS-CoV-2 continuously accumulate amino acid changes, some of which affect transmissibility, virulence or improve the virus’ ability to escape host immunity. Since the beginning of the SARS-CoV-2 pandemic, multiple lineages with concerning phenotypi...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60231-4 |
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| author | Katrina Norwood Zhi-Luo Deng Susanne Reimering Gary Robertson Mohammad-Hadi Foroughmand-Araabi Sama Goliaei Martin Hölzer Frank Klawonn Alice C. McHardy |
| author_facet | Katrina Norwood Zhi-Luo Deng Susanne Reimering Gary Robertson Mohammad-Hadi Foroughmand-Araabi Sama Goliaei Martin Hölzer Frank Klawonn Alice C. McHardy |
| author_sort | Katrina Norwood |
| collection | DOAJ |
| description | Abstract Rapidly evolving viral pathogens such as SARS-CoV-2 continuously accumulate amino acid changes, some of which affect transmissibility, virulence or improve the virus’ ability to escape host immunity. Since the beginning of the SARS-CoV-2 pandemic, multiple lineages with concerning phenotypic alterations, so-called Variants of Concern (VOCs), have emerged and risen to predominance. To optimize public health management and ensure the continued efficacy of vaccines, the early detection of such variants is essential. Therefore, large-scale viral genomic surveillance programs have been initiated worldwide, with data being deposited in public repositories in a timely manner. However, technologies for their continuous interpretation are lacking. Here, we describe the CoVerage system ( www.sarscoverage.org ) for viral genomic surveillance, which continuously predicts and characterizes emerging potential Variants of Interest (pVOIs) from country-wise lineage frequency dynamics, together with their antigenic and evolutionary alterations utilizing the GISAID viral genome resource. In a comprehensive assessment of VOIs, VUMs, and VOCs, we demonstrate how CoVerage can be used to swiftly identify and characterize such variants, with a lead time of almost three months relative to their WHO designation. CoVerage can facilitate the timely identification and assessment of future SARS-CoV-2 variants relevant for public health. |
| format | Article |
| id | doaj-art-b63b2190079b426b8db5b2b9f7bc9ea0 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-b63b2190079b426b8db5b2b9f7bc9ea02025-08-20T03:05:10ZengNature PortfolioNature Communications2041-17232025-07-0116111710.1038/s41467-025-60231-4In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 variants of interestKatrina Norwood0Zhi-Luo Deng1Susanne Reimering2Gary Robertson3Mohammad-Hadi Foroughmand-Araabi4Sama Goliaei5Martin Hölzer6Frank Klawonn7Alice C. McHardy8Computational Biology of Infection Research, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchGenome Competence Center (MF1), Robert Koch InstituteBiostatistics, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchAbstract Rapidly evolving viral pathogens such as SARS-CoV-2 continuously accumulate amino acid changes, some of which affect transmissibility, virulence or improve the virus’ ability to escape host immunity. Since the beginning of the SARS-CoV-2 pandemic, multiple lineages with concerning phenotypic alterations, so-called Variants of Concern (VOCs), have emerged and risen to predominance. To optimize public health management and ensure the continued efficacy of vaccines, the early detection of such variants is essential. Therefore, large-scale viral genomic surveillance programs have been initiated worldwide, with data being deposited in public repositories in a timely manner. However, technologies for their continuous interpretation are lacking. Here, we describe the CoVerage system ( www.sarscoverage.org ) for viral genomic surveillance, which continuously predicts and characterizes emerging potential Variants of Interest (pVOIs) from country-wise lineage frequency dynamics, together with their antigenic and evolutionary alterations utilizing the GISAID viral genome resource. In a comprehensive assessment of VOIs, VUMs, and VOCs, we demonstrate how CoVerage can be used to swiftly identify and characterize such variants, with a lead time of almost three months relative to their WHO designation. CoVerage can facilitate the timely identification and assessment of future SARS-CoV-2 variants relevant for public health.https://doi.org/10.1038/s41467-025-60231-4 |
| spellingShingle | Katrina Norwood Zhi-Luo Deng Susanne Reimering Gary Robertson Mohammad-Hadi Foroughmand-Araabi Sama Goliaei Martin Hölzer Frank Klawonn Alice C. McHardy In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 variants of interest Nature Communications |
| title | In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 variants of interest |
| title_full | In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 variants of interest |
| title_fullStr | In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 variants of interest |
| title_full_unstemmed | In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 variants of interest |
| title_short | In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 variants of interest |
| title_sort | in silico genomic surveillance by coverage predicts and characterizes sars cov 2 variants of interest |
| url | https://doi.org/10.1038/s41467-025-60231-4 |
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