Raw signals of nucleotides in the S gene of 12 SARS-COV-2 strains

The surveillance of the SARS-CoV-2 genome has become one of the crucial techniques in the management of COVID-19, aiding the pandemic response and supporting effective public health interventions. Typically, whole genomic sequencing is used along with PCR-based target enrichment techniques to identify SARS-CoV-2 variants, which is a complicated and time-consuming process that requires central laboratory facilities. Thus, there is an urgent requirement for developing rapid and cost-effective tools that can precisely detect and identify SARS-CoV-2 strains on-site. In this study, we demonstrate the diagnosis of COVID-19 patients and rapid identification of SARS-CoV-2 variants by amplifying and sequencing the entire length of the SARS-CoV-2 S gene using an isothermal enzymatic recombinase amplification combined with the most advanced Oxford nanopore sequencing. The entire procedure, from sampling to sequencing, takes less than 8 hours and can be performed with limited resources. The newly d..., 1/ Sampling and RNA extraction
SARS-CoV-2 strains were isolated from the positive COVID-19 samples and used to extract genomic RNA using the QIAamp Viral RNA Mini Kit (Qiagen, Germany).
2/ Primer design and colorimetric triplex RT-ERA assay

The ERA primers were designed to amplify the entire sequence of the SARS-CoV-2 S gene based on the complete genome of the Wuhan-Hu-1 SARS-CoV-2 strain (NCBI Reference Sequence: MN908947.3). The length of all primers was optimized to be between 28-30 bp, producing amplicons ranging from 300-500 bp, which is suitable for ERA. A total of 12 primer pairs were designed to amplify the full region of the SARS-CoV-2 S gene.
The reaction mixture (15 μl) was prepared in a 0.2 ml tube, consisting of 1.5 μl primer mixture, 6 μl of the ERA buffer, 1 μl of template, and 6.3 μl of water. The reactions were incubated at 39°C for 40 minutes in a heat block. 

3/ Sequencing and bioinformatic analysis

The RT-ERA products for each sample were pooled and purified using..., The raw data can be read using Guppy Basecaller (https://github.com/asadprodhan/GPU-accelerated-guppy-basecalling). Then, fastq files will be generated and used for further analysis steps such as quality check, assembly, comparative analysis.