Back

Detection of SARS-CoV-2 in saliva using tailed amplicon sequencing

Garoutte, A.; Santiago-Rodriguez, T. M.; Fehling, H. L.; Iwasiow, R.

2021-05-21 health informatics
10.1101/2021.05.19.21257217 medRxiv
Show abstract

The most recent virus from the Coronaviridae family infecting humans, SARS-CoV-2, has resulted in a global pandemic. As part of the surveillance efforts, SARS-CoV-2 genomes are increasingly being made publicly available. Methods that include both short- and long-read sequencing have been used to elucidate SARS-CoV-2 genomes; however, many of these untargeted approaches may require deeper sequencing for greater genome coverage. For this reason, sequence capture or amplicon-based approaches for SARS-CoV-2 genome sequencing have been developed. The present study evaluated a modified sequence capture approach, namely, tailed amplicon sequencing, to determine SARS-CoV-2 near complete genome sequences from the saliva of infected individuals. Particularly, the suitability of saliva samples stored at room temperature using OMNIgene(R)*ORAL OME-505 was evaluated. The tailed amplicon sequencing approach poses the additional advantage of being a cost-effective method for library preparation. Different known SARS-CoV-2 variants were identified across the infected subjects, with an average of > 99.4% genome coverage. This methodology also enabled robust genomic surveillance using phylogenetic analyses. The present study supports the suitability of saliva stored at room temperature using collection devices for SARS-CoV-2 variant detection. Importantly, the present study supports the use of tailed amplicon sequencing approaches as an alternative, cost-effective method for SARS-CoV-2 detection in saliva for genomic surveillance.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.