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Baaijens, Jasmijn A; Zulli, Alessandro; Ott, Isabel M; Nika, Ioanna; van der Lugt, Mart J; Petrone, Mary E; Alpert, Tara; Fauver, Joseph R; Kalinich, Chaney C; Vogels, Chantal B F; Breban, Mallery I; Duvallet, Claire; McElroy, Kyle A; Ghaeli, Newsha; Imakaev, Maxim; Mckenzie-Bennett, Malaika F; Robison, Keith; Plocik, Alex; Schilling, Rebecca; Pierson, Martha; Littlefield, Rebecca; Spencer, Michelle L; Simen, Birgitte B; Hanage, William P; Grubaugh, Nathan D; Peccia, Jordan; Baym, Michael
Genome Biology, 11/2022, Letnik: 23, Številka: 1Journal Article
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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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
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Vir: Osebne bibliografije
in: SICRIS
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