Nanopore Sequencing SARS-CoV-2 genome in Qatar

Abstract

Background: The current pandemic, COVID-19, is cause by an RNA coronavirus that was recently identified as SARS-CoV-2. RNA viruses tend to have a high mutation rate; the rate is around a million times greater than that of their hosts. The mutagenic potential of the virus depends on many factors, including the fidelity of nucleic acid-replicating viral enzymes, such as SARS-CoV-2 RNA dependent RNA polymerase (RdRp). The rate of mutation drives viral evolution and genome variability, consequently allowing viruses to escape the immunity of the host and develop resistance to drugs. Therefore, the characterization of SARS-CoV-2 variants might lead to implement better therapeutics treatments, vaccines design and identify new diagnostics approaches. Aim: The aim of this study was to establish a fast sequencing method to identify SARS-CoV-2 mutations in Qatar. This will help to assess if there are new viral variants that are spreading in country. Methods: RNA was isolated from samples collected from Qatar COVID-19 positive patients. The Artic Network V3 primer scheme and Oxford Nanopore ligation sequencing kit were used to prepare the sequencing libraries. Libraries were loaded on to R9.4.1 flow cells and ran on a GridION. Bioinformatics analysis was done following the Artic Network SARA-CoV-2 bioinformatics tools. Results: Genome coverage of sequenced samples was >80% and the depth was average at 200x. The coverage was highly dependable on sample viral load; samples of CT value lower than 30 resulted in better sequence coverage. The sequenced genomes were deposited in GISAID and were mainly clustering with genomes deposited from the UK. Sequences were compared to Illumina and sanger sequences and they showed compatible results. Conclusion: The use of ONT to sequence SARA-CoV-2 is a quick, affordable, and reliable technique to determine viral mutation. Using this technique, the first sequences from Qatar were deposited in to GISAID. Up to date, 700 genomes have been sequences from Qatari samples.