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

Abstract

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.