Sars-Cov-2 Variant Identification Using a Genome Tiling Array and Genotyping Probes

Abstract

With over 5.5 million deaths worldwide attributed to the respiratory disease COVID-19 caused by the novel coronavirus SARS-CoV-2, it is essential that continued efforts be made to track the evolution and spread of the virus globally. The authors previously presented a rapid and cost-effective method to sequence the entire SARS-CoV-2 genome with 95% coverage and 99.9% accuracy. This method is advantageous for identifying and tracking variants in the SARS-CoV-2 genome compared with traditional short-read sequencing methods which can be time-consuming and costly. Herein, the addition of genotyping probes to a DNA chip that targets known SARS-CoV-2 variants is presented. The incorporation of genotyping probe sets along with the advent of a moving average filter improved the sequencing coverage and accuracy of the SARS-CoV-2 genome.

Plain language summary

Throughout the COVID-19 pandemic the virus known as SARS-CoV-2 has continued to mutate and evolve. It is imperative to continue to track these mutations and where the virus has traveled to best inform healthcare practices and global strategies to combat the virus. The authors previously developed a method to investigate 95% of this viral genome with 99.9% accuracy that was more cost-effective and less time-consuming than previous methods. In this work, specific markers were added to the technology to allow tracking of mutations in the virus that have already been documented. In doing so, the accuracy and how much of the viral genome can be sequenced was improved.

Tweetable abstract

SARS-CoV-2 genome tiling array helping to combat the #COVID-19 pandemic by tracking viral evolution in a rapid and cost-effective manner with 99.59% genome coverage and 99.98% accuracy.

Keywords::

• bioinformatics
• COVID-19
• genotyping
• pandemic
• SARS-CoV-2
• screening
• sequencing
• tiling-array
• viral genome

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.tandfonline.com/doi/suppl/10.2217/pme-2022-0013

Author contributions

R Shimada and EN Alden contributed equally to this work. K Hoff and X Ding conducted the experiments. J Sun designed and constructed the genome arrays. R Shimada, EN Alden and AM Halasz developed the base calling methods. JS Edwards and W Zhou conceived and directed the study. All authors drafted the manuscript.

Acknowledgments

The authors gratefully acknowledge the authors found in Supplementary Table 1 from the originating laboratories responsible for obtaining the specimens and the submitting laboratories where genetic sequence data were generated and shared via the Global Initiative on Sharing Avian Influenza Data initiative, on which this research is based.

Financial & competing interests disclosure

The authors declare the following competing financial interest(s): The Centrillion affiliated authors are employees of the company and the company is commercializing the work described herein. This research was partially supported by UNM Comprehensive Cancer Center Support Grant NCI (P30CA118100) and an Institutional Development Award from the National Institute of General Medical Sciences of the National Institutes of Health (P20GM103451). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Data & materials availability

The R scripts along with a test dataset were deposited in a Github repository publicly available at https://github.com/JSElabUNM/COVID_genotyping_probes/