Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques
Jasmijn A. Baaijens (TU Delft - Electrical Engineering, Mathematics and Computer Science, Harvard Medical School)
Alessandro Zulli (Yale University)
Isabel M. Ott (Yale University)
Ioanna Nika (Student TU Delft)
Mart J. van der Lugt (Student TU Delft)
Mary E. Petrone (Yale University)
Tara Alpert (Yale University)
Joseph R. Fauver (Yale University, University of Nebraska Medical Center)
Chaney C. Kalinich (Yale University)
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Abstract
Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable.
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