RNA viruses, like SARS-CoV-2, depend on their RNA-dependent RNA polymerases (RdRp) for replication, which is error prone. Monitoring replication errors is crucial for understanding the virus’s evolution. Current methods lack the precision to detect rare de novo RNA mutations, particularly in low-input samples such as those from patients. Here we introduce a targeted accurate RNA consensus sequencing method (tARC-seq) to accurately determine the mutation frequency and types in SARS-CoV-2, both in cell culture and clinical samples. Our findings show an average of 2.68 × 10⁻⁵ de novo errors per cycle with a C > T bias that cannot be solely attributed to APOBEC editing. We identified hotspots and cold spots throughout the genome, correlating with high or low GC content, and pinpointed transcription regulatory sites as regions more susceptible to errors. tARC-seq captured template switching events including insertions, deletions and complex mutations. These insights shed light on the genetic diversity generation and evolutionary dynamics of SARS-CoV-2.

RNA 病毒，如 SARS-CoV-2，依赖于 RNA 依赖性 RNA 聚合酶 (RdRp) 进行复制，这很容易出错。监控复制错误对于了解病毒的进化至关重要。目前的方法缺乏检测罕见的从头 RNA 突变的精度，特别是在低输入样本（例如来自患者的样本）中。在这里，我们介绍了一种有针对性的精确 RNA 共有测序方法 (tARC-seq)，用于准确确定细胞培养物和临床样本中 SARS-CoV-2 的突变频率和类型。我们的研究结果显示，每个周期平均有 2.68 × 10 ^-5的从头错误，且存在 C > T 偏差，这不能仅仅归因于 APOBEC 编辑。我们确定了整个基因组中与高或低 GC 含量相关的热点和冷点，并确定了转录调控位点作为更容易出错的区域。 tARC-seq 捕获模板切换事件，包括插入、删除和复杂突变。这些见解揭示了 SARS-CoV-2 的遗传多样性产生和进化动态。