  During the SARS-CoV-2 pandemic, wastewater-based genomic surveillance (WWGS)
emerged as an efficient viral surveillance tool that takes into account
asymptomatic cases and can identify known and novel mutations and offers the
opportunity to assign known virus lineages based on the detected mutations
profiles. WWGS can also hint towards novel or cryptic lineages, but it is
difficult to clearly identify and define novel lineages from wastewater (WW)
alone. While WWGS has significant advantages in monitoring SARS-CoV-2 viral
spread, technical challenges remain, including poor sequencing coverage and
quality due to viral RNA degradation. As a result, the viral RNAs in wastewater
have low concentrations and are often fragmented, making sequencing difficult.
WWGS analysis requires advanced computational tools that are yet to be
developed and benchmarked. The existing bioinformatics tools used to analyze
wastewater sequencing data are often based on previously developed methods for
quantifying the expression of transcripts or viral diversity. Those methods
were not developed for wastewater sequencing data specifically, and are not
optimized to address unique challenges associated with wastewater. While
specialized tools for analysis of wastewater sequencing data have also been
developed recently, it remains to be seen how they will perform given the
ongoing evolution of SARS-CoV-2 and the decline in testing and patient-based
genomic surveillance. Here, we discuss opportunities and challenges associated
with WWGS, including sample preparation, sequencing technology, and
bioinformatics methods.
