RCoV19: A One-Stop Hub for SARS-CoV-2 Genome Data Integration, Variant Monitoring, and Risk Pre-Warning

Figures

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  Figure 1. Logical architecture diagram of RCoV19 database

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  Figure 2. Framework of genome data curation model for SARS-CoV-2 RCoV19 integrates genome data from different repositories and provides value-added curations. It collects metadata and genome sequences from different resources, standardizes metadata, and performs de-redundancy processing based on metadata and sequence comparisons. These steps have been chained together as one workflow, which is activated automatically every day and sends the integration statistics to mobile phone client at the end. After integration, RCoV19 performs a series of assessments; it determines completeness of the protein-coding region, assesses sequence quality in five aspects, and defines high-quality sequences. We consider a sequence to be of high quality if it could pass quality control for both Ns (<= 15) and degenerate bases (<= 50). Otherwise, it is of low quality.

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  Figure 3. The monitoring platform of SARS-CoV-2 sequence growth globally and regionally A. The dynamic growth curves of globally and China released SARS-CoV-2 genome sequences, and globally released complete genome sequences as of October 9, 2023. The vertical coordinate with the blue hue on the right-hand side represents China’s data. B. A bar chart shows the top 10 countries with the most public released genome sequences as of October 9, 2023. C. A tabular table shows the statistics of sequences in country/region.

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  Figure 4. Monitoring of SARS-CoV-2 lineage evolution globally and regionally A. Number of released genome sequences and the median mutation frequency along sequence sampling date. The mutation frequency is calculated by dividing the total mutation of each sequence by the genome length. B. Stacking diagram showing the proportion of top 3 prevalent Pango lineages or WHO-defined variants per week. VUMs are currently circulating variants under monitoring that include BA.2.75, CH.1.1, XBB*, XBB.1.9.1, XBB.1.9.2, and XBB.2.3 Pango lineages as of 19 June 2023. Meanwhile, VOIs are currently circulating variants of interest that include XBB.1.5 and XBB.1.16 Pango lineages as of 27 June 2023. C. Heatmap showing the frequency of the cumulative sequences for randomly selected lineages in China. D. Distribution of the number of genome sequences released in China along sequence sampling date. The blue vertical line represents the number of genome sequences released daily in China, and the green line represents the dynamic accumulation of SARS-CoV-2 genome sequences released in China. E. Pie chart showing the lineage proportions in Hubei Province in China from July 1 to October 24 in 2023. VUM, variant…

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  Figure 5. Pre-warning of potential high-risk haplotypes and lineages A. A screenshot of the tabular table for all haplotypes with values of haplotype network features and their risk scores predicted on May 31, 2023. B. Boxplot showing the predicated risk scores for all haplotypes of higher-risk lineages (20 at most). As of May 31, 2023, 12 lineages have been predicted as potential high-risk lineages. C. Distribution of the historical risk scores for user selected lineages. D. Genomic prevalence of lineages based on sequence sampling date.

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  Figure 6. Mutation spectrum comparison among selected lineages and sequences A. Lineage mutation comparison on the S gene among top 3 prevalence lineages in the 10th week of 2023 (XBB.1, XBB.1.5, and BQ.1) and previous VOCs defined by WHO (Alpha, Beta, Gamma, Delta, and Omicron) with mutation frequency. B. Lineage mutation comparison on the N gene among the top 3 prevalent lineages in the 10th week of 2023 (XBB.1, XBB.1.5, and BQ.1) and previous VOCs defined by WHO (Alpha, Beta, Gamma, Delta, and Omicron) with mutation frequency. C. Sequence mutation comparison among sequences (XBB: EPI_ISL_15854782; BJ.1: EPI_ISL_14891585; BM.1.1.1: EPI_ISL_14733830) presented with a heatmap by differential mutations (refers to those after removing common mutations among sequences) in each sequence. The mutations in red color represent possible recombination breakpoints. VOC, variant of concern.

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  Figure 7. Mutation effects on SARS-CoV-2 viral characteristics A. Collection of mutation effect knowledge. The X-axis represents the number of mutations. B. Mutations occurring in the regions encoding experimentally verified T cell epitopes. The magnitude of the circles represents the number of mutations occurring in the regions encoidng each epitope, and different colors indicate T cell epitopes on different proteins encoded by SARS-CoV-2 genome.