ArticleVirus evolution2025
Diverse patterns of intra-host genetic diversity in chronically infected SARS-CoV-2 patients.
Article in Virus evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Identification and masking of artifactual and misleading within-host variants in deep-sequencing SARS-CoV-2 data.Molecular biology and evolution · 2026Article
- Slowdown of synonymous substitution rate preceding the emergence of multiple SARS-CoV-2 variants.Virus evolution · 2026Article
- SARS-CoV-2 genomic diversity and within-host evolution in individuals with persistent infection in the UK: an observational, longitudinal, population-based surveillance study.The Lancet. Microbe · 2025Observational
- Immune pressure is key to understanding observed patterns of respiratory virus evolution in prolonged infections.Virus evolution · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In rare individuals with a severely immunocompromised system, chronic infections of SARS-CoV-2 may develop, where the virus replicates in the body for months. Sequencing of some chronic infections has uncovered dramatic adaptive evolution and fixation of mutations reminiscent of lineage-defining mutations of variants of concern (VOCs). This has led to the prevailing hypothesis that VOCs emerged from chronic infections. To examine the mutation dynamics and intra-host genomic diversity of SARS-CoV-2 during chronic infections, we focused on a cohort of nine immunocompromised individuals with chronic infections and performed longitudinal sequencing of viral genomes. We showed that sequencing errors may cause erroneous inference of genetic variation, and to overcome this, we used duplicate sequencing across patients and time points, allowing us to distinguish errors from low-frequency mutations. We further found recurrent low-frequency mutations that we flagged as most likely sequencing errors. This stringent approach allowed us to reliably infer low-frequency mutations and their dynamics across time. We applied a generalized linear model that accounts for gradual mutation accumulation and episodic divergence shifts to infer a synonymous mutation rate of 1.9 × 10
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