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 pandemic and establishment of SARS-CoV-2 as a human pathogen, multiple lineages with concerning phenotypic alterations, so called Variants of Concern (VOCs), have emerged and risen to predominance. To optimize public health management and to ensure the continued efficacy of vaccines, the early detection of such variants of interest 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 currently lacking. Here, we describe the CoVerage system (www.sarscoverage.org) for viral genomic surveillance, which continuously predicts and characterizes novel and 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, VOCs and VUMs identified, we demonstrate how CoVerage can be used to swiftly identify and characterize such variants, with a lead time of almost three months relative to them reaching their maximal abundances. CoVerage can facilitate the timely identification and assessment of future SARS-CoV-2 variants relevant for public health.