“…In the context of COVID-19, pooling strategies have been explored as potential ways to increase testing capacities. Bloom et al use simple pooling to detect SARS-CoV-2 through next-generation sequencing instead of the traditional RT-PCR procedure 18 . Libin et al explore the use of a pooling strategy that groups individuals that belong to the same household 19 .…”
Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.
“…In the context of COVID-19, pooling strategies have been explored as potential ways to increase testing capacities. Bloom et al use simple pooling to detect SARS-CoV-2 through next-generation sequencing instead of the traditional RT-PCR procedure 18 . Libin et al explore the use of a pooling strategy that groups individuals that belong to the same household 19 .…”
Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.
“…Additional advancements to other sequencing strategies (e.g., NovaSeq with ~ 10 billion reads), incorporating automated workflows for library preparation, and using multiple machines in parallel would further improve testing capacity. While the theoretical throughput would be comparable to other targeted deep sequencing-based diagnostics (e.g., SARS-Seq, SwabSeq, C19-SPAR-Seq) [ 16 , 19 , 20 ], the increased genomic and sequence space coverage provided by DeepSARS enables genomic surveillance. DeepSARS provides genomic surveillance by recovering sequencing information for approximately 20% of the SARS-CoV-2 genome.…”
Section: Discussionmentioning
confidence: 99%
“…However, these methods currently rely upon selectively sequencing only a few conserved regions of the SARS-CoV-2 genome. For example, the SARSseq method described by Yelagandula et al, relies on only sequencing two short regions (~ 70 bp) of the N gene [ 19 ]; similarly, Bloom et al developed SwabSeq which targets only a single, minimal region (26 bp) of the S gene of SARS-CoV-2 [ 20 ]. Aynaud et al established the C19-SPAR-Seq method for targeted sequencing of five regions across the viral genome, however, only one of them corresponds to the spike protein’s receptor binding domain (RBD) [ 16 ], where characteristic mutations associated with VoC are found [ 6 ].…”
Background
The continued spread of SARS-CoV-2 and emergence of new variants with higher transmission rates and/or partial resistance to vaccines has further highlighted the need for large-scale testing and genomic surveillance. However, current diagnostic testing (e.g., PCR) and genomic surveillance methods (e.g., whole genome sequencing) are performed separately, thus limiting the detection and tracing of SARS-CoV-2 and emerging variants.
Results
Here, we developed DeepSARS, a high-throughput platform for simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2 by the integration of molecular barcoding, targeted deep sequencing, and computational phylogenetics. DeepSARS enables highly sensitive viral detection, while also capturing genomic diversity and viral evolution. We show that DeepSARS can be rapidly adapted for identification of emerging variants, such as alpha, beta, gamma, and delta strains, and profile mutational changes at the population level.
Conclusions
DeepSARS sets the foundation for quantitative diagnostics that capture viral evolution and diversity.
Graphical abstract
DeepSARS uses molecular barcodes (BCs) and multiplexed targeted deep sequencing (NGS) to enable simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2. Image was created using Biorender.com.
“… [25] , [26] , [27] During the extraction processes, necessary toxic RNA isolation reagents are needed. [28] , [29] , [30] , [31] To better protect the environment, finding strategies to reduce the release of toxic substances becomes the need for new strategies. Fig.…”
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