Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning

Abstract

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network's behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.

Figure 1

On the left, (a) shows the proposed workflow for the automated design of primers for viruses. On the right, (b) summarizes the different experiments reported in the paper, along with the datasets used in each trial.

Figure 2

Confusion Matrix of the 10-fold stratified cross-validation for the CNN classifier in the original 553 SARS-CoV-2 sequences.

Figure 3

Input 3a, and output 3b of the methodology in colored pixels represent bases: G = green, C = blue, A = red, T = orange, missing = black. The data is separated by class SARS-CoV1: SARS-CoV, SARS-CoV P2, SARS-CoV HKU-39849 and SARS-CoV GDH-BJH01. For visualization purposes we do not show HCov-EMC and HCoV-4408, given the number of samples. From visual inspection, it is possible to notice the similarity of patterns between samples belonging to the same class.

Figure 4

Laboratory validation of the candidate primer set by conventional PCR. MM, molecular weight marker; lanes 1–8, 10-fold dilutions of SARS-CoV-2 RNA (corresponding to Ct values 26–39 in the diagnostic reference assay); lanes 9–14, RNA from different human coronaviruses (hCoV-OC43, hCoV-229E, hCoV-NL63, MERS-CoV, SARS-1, SARS-CoV-2, respectively); lanes 15, 16, 17, 19, 20, 21, patient samples previously found positive for SARS-CoV-2; lanes 18, 22, 23, 24, patient samples previously found negative for SARS-CoV-2.

Figure 5

Graphical representation of the architecture of the CNN used in the experiments.