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

Recurrent artefactual within-host SARS-CoV-2 variants are common and can distort diversity and transmission estimates, even at low minor allele frequencies.

• Why it matters: Accurate identification of low-frequency variants is crucial for understanding viral evolution and transmission, but systematic artefacts threaten the reliability of these analyses.
• What they did: Analyzing large-scale sequencing data from the UK's COVID-19 Infection Survey, the study developed a dataset-aware framework to identify and mask recurrent artefactual variants specific to sequencing centers.
• The result: Implementing this framework reduces false sharing of variants and significantly improves the accuracy of within-host diversity and transmission inferences, emphasizing the need for artefact control in viral genomics.