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Enhanced Detection of Recently Emerged SARS-CoV-2 Variants of Concern in Wastewater

Sapoval, N.; Lou, E.; Hopkins, L.; Ensor, K. B.; Schneider, R.; Treangen, T.; Stadler, L.

2021-09-12 infectious diseases
10.1101/2021.09.08.21263279 medRxiv
Show abstract

As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variants of concern (VoC) in communities. Multiple recent studies support that wastewater-based SARS-CoV-2 detection of circulating VoC can precede clinical cases by up to two weeks. Furthermore, wastewater based epidemiology enables wide population-based screening and study of viral evolutionary dynamics. However, highly sensitive detection of emerging variants remains a complex task due to the pooled nature of environmental samples and genetic material degradation. In this paper we propose quasi-unique mutations for VoC identification, implemented in a novel bioinformatics tool (QuaID) for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3 week earlier VoC detection compared to existing approaches, (ii) enables more sensitive VoC detection, which is shown to be tolerant of >50% mutation drop-out, and (iii) leverages all mutational signatures, including insertions & deletions.

Matching journals

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

1
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18.8%
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2
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3
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Top 0.1%
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6
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4.1%
7
Nature Computational Science
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Top 0.2%
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8
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Top 0.5%
2.8%
9
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○ 1154 papers in training set
Top 9%
2.7%
10
Nature Microbiology
○ 155 papers in training set
Top 1%
2.4%
11
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● 469 papers in training set
Top 6%
2.1%
12
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○ 165 papers in training set
Top 1%
1.8%
13
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● 225 papers in training set
Top 2%
1.5%
14
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○ 645 papers in training set
Top 8%
1.5%
15
PLOS Computational Biology
● 1863 papers in training set
Top 16%
1.4%
16
Genome Medicine
○ 183 papers in training set
Top 3%
1.4%
17
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● 17 papers in training set
Top 0.3%
1.1%
18
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Top 2%
1.1%
19
Cell Reports
○ 1498 papers in training set
Top 25%
1.0%
20
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○ 186 papers in training set
Top 3%
0.9%
21
Communications Biology
○ 993 papers in training set
Top 29%
0.9%
22
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Top 9%
0.6%
23
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◐ 354 papers in training set
Top 7%
0.6%
24
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Top 43%
0.6%
25
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Top 1%
0.6%
26
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○ 14 papers in training set
Top 0.4%
0.6%

"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.