Refining SARS-CoV-2 intra-host variation by leveraging large-scale sequencing data

Pillar 6
Computational Analysis, Modelling and Evolutionary Outcomes (CAMEO)

Understanding how the SARS-CoV-2 virus evolves within individuals during infection is key to managing the pandemic and preventing future outbreaks. In our research, we used publicly available sequencing data, originally collected for genomic surveillance, to study how the virus mutates within individual hosts. These mutations, called intra-host single nucleotide variants (iSNVs), are genetic changes that can shed light on how the virus adapts.

Working with such large datasets brings the challenge coming from the diversity of data sources collected over different time periods, locations, and laboratory protocols. To address this, we developed a robust quality control framework that accounts for these factors, which uses key aspects of sequencing data and advanced visualization techniques like PHATE, to identify and eliminate potential artifacts. This ensures the accuracy of our findings and helps us better understand how the virus evolves within the host.

This knowledge is critical for predicting potential new variants from existing data and mitigating future viral threats, especially in the context of ongoing global health challenges related to SARS-CoV-2 and other emerging viruses.

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