A benchmarking study of SARS-CoV-2 whole-genome sequencing protocols using COVID-19 patient samples

iScience

David Turay¹  Charles Wang¹  Maryam Hosseini²  Wendell Jones³  Zhaowei Yang³  Jing Li⁴  Wanqiu Chen⁴  Tiantian Liu⁴  Zhong Chen⁴  Diana Ho⁴  Xin Chen⁴  Ciprian P. Gheorghe⁵ 

[1] Department of Allergy and Clinical Immunology, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, People's Republic of China;;Division of Microbiology &Molecular Genetics, Department of Basic Science, School of Medicine, Loma Linda University, Loma Linda, CA, USA;Center for Genomics, School of Medicine, Loma Linda University, Loma Linda, CA, USA;;Division of Microbiology &

关键词: Biological sciences;    Molecular biology;    Microbiology;    Virology;    omics;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging new type of coronavirus that is responsible for the COVID-19 pandemic and the unprecedented global health emergency. Whole-genome sequencing (WGS) of SARS-CoV-2 plays a critical role in understanding the disease. Performance variation exists across SARS-CoV-2 viral WGS technologies, but there is currently no benchmarking study comparing different WGS sequencing protocols. We compared seven different SARS-CoV-2 WGS library protocols using RNA from patient nasopharyngeal swab samples under two storage conditions with low and high viral inputs. We found large differences in mappability and genome coverage, and variations in sensitivity, reproducibility, and precision of single-nucleotide variant calling across different protocols. For certain amplicon-based protocols, an appropriate primer trimming step is critical for accurate single-nucleotide variant calling. We ranked the performance of protocols based on six different metrics. Our findings offer guidance in choosing appropriate WGS protocols to characterize SARS-CoV-2 and its evolution.

【 授权许可】

Unknown