RNA sequencing has become an essential tool in tracking the spread and evolution of COVID-19, especially through wastewater surveillance. Wastewater contains fragments of SARS-CoV-2 RNA shed by infected individuals, providing a way to monitor the virus in entire communities. By sequencing this viral RNA, researchers can identify the specific variants of the virus that are circulating, helping public health officials respond to outbreaks more effectively.
While techniques like quantitative PCR (qPCR) can estimate the amount of viral RNA in wastewater, RNA sequencing allows scientists to go further by identifying the different variants present in a sample. However, predicting the emergence of new, unknown variants is a complex challenge. Wastewater samples contain a mixture of genetic material, and full viral genomes are often incomplete. This is where RNA sequencing becomes crucial—researchers at the University of Waterloo have developed a method that uses non-negative matrix factorization of RNA sequencing data from wastewater to detect known variants and potentially identify new ones.
Overview of the proposed method for determining viral variant genomes from wastewater samples
By analyzing RNA sequences from multiple wastewater samples, this method can reconstruct the genetic patterns of COVID-19 variants, even detecting sub-lineages like BA.5.2.1. Importantly, this technique doesn’t rely on clinical data from patients, meaning it can provide early warnings of new variants simply by analyzing wastewater. This is a game-changer for monitoring not only COVID-19 but also other viral pathogens in the future.
RNA sequencing in wastewater surveillance offers a powerful way to track the spread of viruses and spot emerging threats. It enables scientists to identify new variants quickly, helping public health officials take timely action to prevent further outbreaks. As our understanding of RNA sequencing grows, this approach could also be used to generate more complete viral genomes, even for viruses with limited clinical data, making it a vital tool in global health efforts.
Availability – the implementation of the method was written in Python. It has been tested on macOS and is available for download at https://github.com/Ellmen/derived-wastewater-lineages.