Identification and masking of artefactual and misleading within-host variants in deep-sequencing SARS-CoV-2 data

Journal article

Deep sequencing data are increasingly used to study within-host viral diversity and to inform evolutionary inference. For SARS-CoV-2, analyses based on intra-host single-nucleotide variants (iSNVs) have been widely applied to quantify within-host diversity and infer transmission dynamics. However, these applications critically depend on the reliable identification of low-frequency variants, which remain vulnerable to systematic and technical artefacts. In this study, we show that recurrent artefactual iSNVs are common in large-scale SARS-CoV-2 sequencing data and can persist even under conservative minor allele frequency (MAF) thresholds. Using data from the UK’s Office for National Statistics COVID-19 Infection Survey, we demonstrate that such artefacts are predominantly sequencing centre- rather than primer-specific. Each centre exhibits a modest, distinct set of recurrent artefactual variants showing little overlap with sites routinely masked at the consensus level. To address this, we developed a systematic, dataset-aware framework that uses recurrence within sequencing datasets to identify small, noise adapted sets of artefactual iSNVs to mask. Applying this framework reduces spurious sharing of low frequency variants between samples and qualitatively alters downstream inferences, including estimates of within-host diversity and transmission bottleneck sizes. Although this study focussed on SARS-CoV-2, it is likely that recurrent artefactual iSNVs will be problematic for other viruses as mass-sequencing becomes increasingly routine. Together, these findings highlight the importance of explicit, dataset-aware artefact control for robust inference from within-host variation, particularly as genomic studies increasingly seek to exploit sub-consensus diversity in rapidly evolving pathogens.

Authors: Anker, KM; Pena, RE; Kemp, SA; Clarke, J; Zhao, L

• Publication status: Accepted
• Peer review status: Peer reviewed
• Publisher: Oxford University Press
• Journal: Molecular Biology and Evolution
• Acceptance date: 2026-07-02
• EISSN: 1537-1719
• ISSN: 0737-4038
• Language: English