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MINERVA: A facile strategy for SARS-CoV-2 whole-genome deep sequencing of clinical samples

Chen Chen; Jizhou Li; Lin Di; Qiuyu Jing; Pengcheng Du; Chuan Song; Jiarui Li; Qiong Li; Yunlong Cao; X. Sunney Xie; Angela R. Wu; Hui Zeng; Yanyi Huang; Jianbin Wang
Molecular Cell 2020;80(6):1123-1134.e4 DOI:10.1016/j.molcel.2020.11.030
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Analyzing the genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from clinical samples is crucial for understanding viral spread and evolution as well as for vaccine development. Existing RNA sequencing methods are demanding on user technique and time and, thus, not ideal for time-sensitive clinical samples; these methods are also not optimized for high performance on viral genomes. We developed a facile, practical, and robust approach for metagenomic and deep viral sequencing from clinical samples. We demonstrate the utility of our approach on pharyngeal, sputum, and stool samples collected from coronavirus disease 2019 (COVID-19) patients, successfully obtaining whole metatranscriptomes and complete high-depth, high-coverage SARS-CoV-2 genomes with high yield and robustness. With a shortened hands-on time from sample to virus-enriched sequencing-ready library, this rapid, versatile, and clinic-friendly approach will facilitate molecular epidemiology studies during current and future outbreaks. The novel coronavirus disease 2019 (COVID-19) pandemic poses a serious public health risk. Chen et al. develop a facile and robust approach for transcriptomic sequencing of COVID-19 samples that will facilitate molecular epidemiology studies during current and future outbreaks.