Mutational landscape of Omicron variant and comparison with the mutations of other VOCs and VOIs

We illustrated the mutational landscape of Omicron and compared the mutations with those of other VOCs. First, a schematic diagram representing all the mutational landscapes throughout the genome and its comparison with VOCs and VOIs was created (Fig. 2A). Our study corroborates previous findings about the immense number of mutations in the Omicron genome compared to other variants. We developed a statistical model using the total number of mutations in the genome. It shows the number of mutations of different VOCs (Alpha, Delta, Gamma, and Beta) and VOIs (Lambda, Mu). In this model, we found all the VOCs and VOIs, including Alpha, Delta, Gamma, Beta, Lambda, and Mu, in a cluster showing the number of mutations other than Omicron. The variant is out of cluster due to more mutations (Fig. 2B). However, from this model, we found that the clustering event formed two groups: the first containing all variants and the second containing only Omicron. Omicron exhibits a high number of mutation patterns compared to other VOCs and VOIs. The mutational landscape of the Omicron variant is presented in Table 1.

Fig. 2

A schematic diagram representing the mutational landscape throughout the genome of Omicron and its comparison with VOCs and VOIs. (A) The mutational landscape throughout the genome of Omicron and its comparison with VOCs and VOIs. (B) A statistical model illustrates the number of total genome mutations in the VOCs and VOI, including Omicron. This model shows the number of mutations in the genome of VOCs and VOIs Alpha, Delta, Gamma, Beta, Lambda, and Mu formed a cluster regarding the number of mutations. Here, the clustering event formed two groups: The first one contains all variants, and the second one is Omicron. Omicron exhibits a high number of mutation patterns compared to other VOCs and VOIs. For the analysis, we have taken the data from the literature collected from open source databases such as Google Scholar [36] and PubMed [37, 38]. At the same time, we also collected the open source mutation information on CDC’s Omicron variants and other VOCs/VOIs [39,40,41]; eCDC[41], WHO[42] etc.

Table 1 Mutational landscape of Omicron variant S-glycoprotein substitutions in the Omicron variant and other VOCs and VOIs

We illustrated the mutational landscape of the Omicron S-glycoprotein. A schematic diagram represents all mutational landscapes throughout the genome and its comparison with VOCs and VOIs (Fig. 3A). We constructed a statistical model using the total number of mutations in the S-glycoprotein. The model showed that all VOCs and VOIs formed a cluster other than Omicron (Fig. 3B). Here, the clustering event also formed two groups: the first group contained all variants and the second group contained only Omicron. Similar to the previous model (developed using the number of total genome mutations), Omicron belongs to a different group and shows a different pattern than other variants regarding the number of mutations.

Fig. 3

A schematic diagram represented the mutational landscape throughout the S-glycoprotein of Omicron and its comparison with VOCs and VOIs. (A) The mutational landscape throughout the S-glycoprotein of Omicron and its comparison with VOCs and VOIs. (B) A statistical model demonstrates the number of mutations in the S-glycoprotein in the VOCs and VOI, including Omicron. This model shows the number of mutations in the S-glycoprotein of VOCs and VOIs Alpha, Delta, Gamma, Beta, Lambda, and Mu formed a cluster regarding the number of mutations. Here, the clustering event also formed two groups: The first one contains all variants, and the second one is Omicron. Like the previous model (developed using the number of total genome mutations), Omicron belongs to a different group and shows a different pattern compared to other variants in terms of the number of mutations. For the analysis, we have taken the data from the literature collected from open source databases such as Google Scholar [36], PubMed[37, 38] etc. At the same time, we also collected the open source mutation information on CDC’s Omicron variants and other VOCs/VOIs [39,40,41]; eCDC [41], WHO[42] etc.

The 3D model deployment of the S-glycoprotein with mutations is significant for the SARS-CoV-2 variant, providing an interactive platform. A 3D model was generated using bioinformatics. The 3D model helps to compare the protein with other variants using 3D space. We developed a 3D model to understand the position of all mutations of S-glycoprotein, the Omicron, and other VOCs and VOIs. Every model was generated to understand every detail of the mutational landscape of the Omicron and other VOCs and VOIs (Alpha, Delta, Gamma, Beta, Lambda, and Mu). We identified mutations of the S-glycoprotein of Omicron in a 3D model (Fig. 4A). Mutations in the RBD of the S-glycoprotein of Omicron were depicted using a 3D model. We found 15 mutations in the RBD region (Fig. 4B). Mutations in the RBM of the S-glycoprotein of Omicron were depicted using a 3D model. We found ten mutations in the RBM region (Fig. 4C). Simultaneously, all mutations of the S-glycoprotein of VOCs (Alpha, Delta, Gamma, and Beta) have been illustrated (Fig. 4D). Mutations of Omicron are a puzzle for researchers, and they have attempted to solve these mutations [54]. Our study is significant in this regard. However, this part of the analysis will assist researchers in understanding the Omicron mutations and those of the Alpha, Delta, Gamma, and Beta variants. The 3D molecular statures of Omicron may help elucidate global takeover events [57]. This structural basis of understanding the Spike protein SARS-CoV-2 will help future researchers further understand this variant’s functional properties. This study informs us of emerging mutations in 3D, and it will assist future researchers in understanding emerging mutations such as E484K, K417T, N501Y, S494P, and L452R for functionality mapping.

Fig. 4

3D model illustrated the mutational landscape throughout the S-glycoprotein of Omicron and its comparison with VOCs and VOIs. (A) A 3D model that illustrates all the mutations of the S-glycoprotein of Omicron. (B) A 3D model that illustrates all the mutations RBD region of the S-glycoprotein of Omicron. (C) A 3D model describes all the mutations RBM of the S-glycoprotein of Omicron. (D) A 3D model that illustrates all the mutations in the VOCs (Delta (B.1.617.2), Alpha (B.1.1.7), Gamma (P.1), Beta (B.1.351)). (E) A 3D model that illustrates all the RBD mutations in the VOCs (Delta (B.1.617.2), Alpha (B.1.1.7), Gamma (P.1), Beta (B.1.351)). (F) A 3D model that illustrates all the mutations in the VOIs (Lambda (C.37) and Mu (B.1.621). (G) A3D model that illustrates all the RBD mutations in the VOIs (Lambda (C.37) and Mu (B.1.621)). All the 3D models were developed using PyMOL software. For a 3D model generation, we used some PDB files (PDB ID: 6VXX)

All the mutations of the RBD of VOCs (Alpha, Delta, Gamma, and Beta) were identified under the particular position of mutations (Fig. 4E). Similarly, a 3D model was developed to comprehend all mutations of the S-glycoprotein of the VOIs (Lambda and Mu) (Fig. 4F). We illustrated the understanding of all RBD mutations of the S-glycoprotein in the VOIs (Fig. 4G). The mutational landscape for the mutations of the S-glycoprotein has been illustrated for all the VOCs using a table (Table 2). The mutational landscape for the important mutations of the S-glycoprotein has been illustrated for the VOIs in Table 3.

Table 2 Emerging mutations in S-protein of VOCs of SARS-CoV-2Table 3 Emerging mutations in S-protein of SARS-CoV-2 VOIsSingle-nucleotide variations (SNVs) of 5'UTR, ORF1a, ORF1ab, S, ORF3a, E, M, ORF6, ORF7a, ORF7b, ORF8, N, and 3′UTR regions of Omicron and comparison other VOCs and VOIs

We analyzed SNVs throughout the genome (including 5′UTR, ORF1a, ORF1ab, S, ORF3a, E, M, ORF6, ORF7a, ORF7b, ORF8, N, and 3′UTR regions) of Omicron and compared them with other VOCs and VOIs.

A comparative analysis of SNVs of the 5′UTR region is illustrated in Fig. 5A. We found that SNV with the highest mutational frequency was C241T. For Omicron, the mutational frequency was 94%. However, the mutational frequency was noted in the case of Delta (99%), Alpha (100%), Beta (100%), Gamma (99%), Lambda (100%), and Mu (99%).

Fig. 5

Single-nucleotide variations (SNVs) of some parts of the genome (5′UTR, ORF1a, ORF1ab, S, ORF3a, E) of Omicron along with other VOCs and VOIs. (A) SNVs of 5′UTR of Omicron along with other VOCs and VOIs. (B) SNVs of ORF1a of Omicron along with other VOCs and VOIs. (C) SNVs of ORF1ab of Omicron along with other VOCs and VOIs. (D) SNVs of S of Omicron along with other VOCs and VOIs. (E) SNVs of ORF3a of Omicron along with other VOCs and VOIs. (F) SNVs of E of Omicron along with other VOCs and VOIs. We have used the COVID-19 CG server for SNVs analysis of these parts of the genome (5′UTR, ORF1a, ORF1ab, S, ORF3a) (E) of Omicron along with other VOCs and VOIs [48]. The server used the data from the GISAID ingestion pipeline, which is open-source data [48]

Similarly, a comparative analysis of SNVs in the ORF1a region is illustrated in (Fig. 5B), showing that SNV with the highest mutational frequency was C3037T. For Omicron, the mutational frequency was 93%. It was 100% for all other VOCs and VOIs.

The comparative analysis of SNVs of the ORF1ab region revealed that the two SNVs with the highest mutational frequencies were C3037T and C14408T (Fig. 5C). For Omicron, the mutational frequencies of these SNVs were 94% and 98%, respectively. However, SNV C3037T was 100% for other VOCs and VOIs. Similarly, the SNV C14408T was 100% for Delta, 100% for Alpha, 90% for Beta, 98% for Gamma, 100% for Lambda, and 99% for Mu.

The comparative analysis of SNVs in the S region is illustrated (Fig. 5D). We found that the SNV with the highest mutational frequency was A23403G. For Omicron, the mutational frequency was 99%. However, it was noted as 100% for all other VOCs and VOIs.

The comparative analysis of SNVs in the ORF3a region is illustrated in Fig. 5E. It was observed that the SNV with the highest mutational frequency of SNV is A23403G. In the case of Omicron, the mutational frequency of the SNV was 99%. It was noted as 100% for all other VOCs and VOIs.

We evaluated the SNVs of the E region (Fig. 5F). We found only one SNV (A26270G) in Omicron, with a mutational frequency of 81%. The SNV was not found for other variants.

For the M region, we found three SNVs for Omicron, A26530G, C26577G, and G26709A, and the mutational frequencies were 41%, 73%, and 90%, respectively (Fig. 6A). SNV T26767T was also found for Delta with an SNV of 100%.

Fig. 6

Single-nucleotide variations (SNVs) of some other parts of the genome (M, ORF6, ORF7a, ORF7b, ORF8, 3′UTR) of Omicron along with other VOCs and VOIs. (A) SNVs of M of Omicron along with other VOCs and VOIs. (B) SNVs of ORF6 of Omicron, along with other VOCs and VOIs. (C) SNVs of ORF7a of Omicron, along with other VOCs and VOIs. (D) SNVs of ORF7b of Omicron, along with other VOCs and VOIs. (E) SNVs of ORF8 of Omicron, along with other VOCs and VOIs. (F) SNVs of N of Omicron along with other VOCs and VOIs. (G) SNVs of 3′UTR of Omicron along with other VOCs and VOIs. We have also used the COVID-19 CG server for SNVs analysis of these parts of the genome (M, ORF6, ORF7a, ORF7b, ORF8, 3′UTR) of Omicron along with other VOCs and VOIs [48]. The server used the data from the GISAID ingestion pipeline, which is open-source data [48]

Only one mutational frequency (91%) was found for Omicron for SNV A27259C (Fig. 6B). For the ORF7a region, we found two SNVs. However, the mutational frequencies were insignificant, and these two SNVs could not be considered (Fig. 6C). Here, we found two SNVs for the Delta variant, T27638C, and C27752T, and the mutational frequencies were the same (97%) in both cases. For the ORF7b region, SNV C27807T was found in Omicron, and the mutational frequency was 66% (Fig. 6D). Another SNV C27874T with a mutational frequency of 77% was identified in Delta variant in this region.

In the ORF78 region, we found no significant SNVs for Omicron (Fig. 6E). Here, we identified three SNVs with the highest mutational frequencies for Alpha (C27972T, G28048T, and A28111G), with mutational frequencies of 99% for all SNVs. We also found the two highest mutational frequencies for Mu (C27925A and C28005T), with mutational frequencies of 99% each. For the M region, the two significant SNVs for Omicron (> 75%) were C28311T and G28881A, with mutational frequencies of 80% and 78%, respectively (Fig. 6F).

Lastly, the 3′UTR region analysis showed no significant SNVs for Omicron (Fig. 6G). However, we found one highly significant SNV for the Delta variant, G29742T, with a mutational frequency of 99%. However, we have tried to analyze mutational study events of ORF6. Nevertheless, the used server failed to generate the mutational event.

Recently, Yadav et al. found the Omicron variant in collected samples, and the genome was analyzed using next-generation sequencing. Finally, several SNVs were found in Omicron clinical isolates [58]. Ahmed et al. found the Omicron variant by sequencing a swab sample collected from the nasopharynx of an aircraft traveler. The Omicron variant was also found in subsequent sequencing (ATOPlex and Nanopore) of aircraft wastewater samples. They have attempted to discuss the genome in light of SNVs. However, researchers have not yet fully analyzed SNVs [59]. However, we performed a comprehensive SNV analysis throughout the genome of Omicron and compared it with other VOCs/VOIs.

Amino acid (AA) variants in the non-structural proteins of Omicron and comparison with mutations in other VOCs and VOIs

We analyzed AA variants in the non-structural proteins (nsp2, nsp3 (PL2-PRO), nsp4, nsp5 (3CLp), nsp6, nsp12 (RdRp), nsp13 (Helicase), and nsp14) of Omicron and compared the mutations with other VOCs and VOIs. No AA variant was found in the Omicron region of nsp2 (Fig. 7A). We found a T85I AA variant with 100% mutational frequency in the Delta variant. Similarly, in nsp3, the maximum mutational frequency of the Omicron variant was 78% in the A1892T AA variant (Fig. 7B). We found a highly significant mutational frequency of the AA variant in three places in the Alpha variant as well, which was 100% at T183I and A890D and 99% at I1412T. We also found the Mu variant mutational frequency of 100% in T720I AA. In nsp4, one AA variant had a significant mutational frequency (99%) among the Omicron variant in T492I (Fig. 7C). Lambda had two AA variants (L438P and T492I) with 100% mutational frequency. Similarly, Mu has one AA variant (T492I) with 100% mutational frequency. In nsp5, we found one AA variant (P132H) with an 83% mutational frequency in the Omicron variant (Fig. 7D). Additionally, we found one AA variant (K90R) with 99% mutational frequency in the Beta variant and one AA variant (G15S) had 97% mutational frequency in the Lambda variant. Similarly, in nsp6, Omicron had one AA variant (I189V) with an 88% mutational frequency (Fig. 7E). We also found one AA variant (Q160R) with a 99% mutational frequency in the Lambda variant.

Fig. 7

Amino acid (AA) variants in the non-structural proteins of Omicron along with other VOCs and VOIs. (A) AA variants in nsp2, (B) AA variants in nsp-3 (PL2-PRO), (C) AA variants in nsp4, (D) AA variants in nsp5(3CLp), (E) AA variants in nsp6, (F) AA variants in nsp12 (RdRp), (G) AA variants in nsp13 (Helicase), (H) AA variants in nsp14. In this part of the study, we have also used the COVID-19 CG server to analyze the amino acid (AA) variants of non-structural proteins of Omicron along with other VOCs and VOIs [48]. The server used the data from the GISAID ingestion pipeline, which is open-source data [48]

In nsp12, we found one AA variant (P323L) with a significant mutational frequency for VOCs and VOIs, including Omicron (Fig. 7F). Mutational frequencies were 100% (Delta), 100% (Alpha), 90% (Beta), 97% (Omicron), 100% (Lambda), and 99% (Mu). However, no AA variant was found in Omicron in the nsp13 region (Fig. 7G). Delta had one AA variant (P77L) with a significant mutational frequency of 94%. Similarly, Mu had one AA variant (P419S) with a mutational frequency of 99%. In contrast, one AA variant was found with a mutational frequency of 81% in Omicron in the nsp114 region (Fig. 7H).

3.5. Amino acid (AA) variants of structural proteins and associated regions (S-glycoprotein, ORF3a, ORF3b, E, M, ORF7a, ORF7b, ORF8, N, ORF9b, and ORF9c) of Omicron and comparison with mutations in other VOCs and VOIs

We evaluated AA variants in structural proteins and associated regions (S-glycoprotein, ORF3a, ORF3b, E, M, ORF7a, ORF7b, ORF8, N, ORF9b, and ORF9c) of Omicron and compared the mutations with other VOCs and VOIs. We found several AA variants in the S-glycoprotein with significant mutational frequency in Omicron. The AA variants with mutational frequency are A67V (75%), HV69 (66%), T95I (84%), GVYY142D (71%), T478K (69%), T547K (74%), D614G (99%), H655Y (91%), N679K (91%), P681H (91%), N764K (63%), D796Y (71%), N856K (74%), Q954H (86%), N969K (81%), L981F (81%), G339D (58%), S371L (40%), S373P (42%), S375F (43%), K417N (26%), N440K (34%), G446S (33%), S477N (40%), T478K (69%), E484A (42%), Q493K (43%), G496S (39%), Q498R (43%), N501Y (44%), and Y505H (43%). Interestingly, one AA variant (D614G) was mutated in all VOCs and VOIs, including Omicron (Fig. 8A). Mutation frequencies were 100% (Delta), 100% (Alpha), 100% (Beta), 99% (Omicron), 100% (Lambda), and 100% (Mu).

Fig. 8

Amino acid (AA) variants in the structural proteins of Omicron along with other VOCs and VOIs. (A) AA variants in S-glycoprotein, (B) AA variants in ORF3a, (C) AA variants in ORF3b, (D) AA variants in E, (E) AA variants in M, (F) AA variants in ORF7a, (G) AA variants in ORF7b, (H) AA variants N, (I) AA variants in ORF8b, (J) AA variants in ORF9b, (K) AA variants in ORF9c. In this part of the study, we have also used the COVID-19 CG server to analyze the amino acid (AA) variants of structural proteins of Omicron along with other VOCs and VOIs [48]. The server used the data from the GISAID ingestion pipeline, which is an open source data [48]

In ORF3a, no AA variant was detected in the Omicron (Fig. 8B). We found S26L (Delta) and Q57H (Mu) AA variants with a 100% mutational frequency. In orf3a, one AA variant (P21S) had a significant mutational frequency (94%) in the Omicron variant (Fig. 8C).

One AA variant (T9I) with 82% mutational frequency was found in Omicron in the M protein (Fig. 8D). Similarly, three AA variants (D3G, Q19E, and A63T) with 39%, 69%, 72% mutational frequencies were found in Omicron in the M protein (Fig. 8E). Simultaneously, one AA variant (I82T) with 100% mutational frequency was found in the M protein of the Delta variant.

In ORF7a, one AA variant (V28A) with meager (10%) mutational frequency in the Omicron variant cannot be considered as an AA variant (Fig. 8F). However, two AA variants (V28A and T120I) were noted, each with 97% mutational frequencies in both cases in the Delta variant.

One AA variant (V28A) with meager (13%) mutational frequency in the Omicron variant in ORF7b cannot be considered an AA variant (Fig. 8G). However, one AA variant (T40I) was noted, with a 76% mutational frequency in the Delta variant.

In ORF8, one AA variant (DF119) with a deficient (7%) mutational frequency in the Omicron variant could not be considered an AA variant (Fig. 8H). However, we found that the same AA variant (DF119) had a significant mutational frequency (86%) in the Delta variant.

Four AA variants (P13L, ERS31, R203K, and G204R) with 78%, 69%, 81%, and 72% mutational frequencies, respectively, were found in Omicron in the N protein (Fig. 8I). Simultaneously, four AA variants (D63G, R203M, G215C, and D377Y) with 99%, 99%, 75%, 99% mutational frequencies respectively were found in the N protein of the Delta variant.

In ORF9b, two AA variants (P10S, ENA27-) with 78% and 69% mutational frequencies were found in Omicron (Fig. 8J). However, we found 99% mutational frequency in one AA variant (T60A) in the Delta variant and one AA variant (Q77E) in the Gamma variant.

Finally, we found one AA variant (G50N) with 69% mutational frequency Omicron in orf9c (Fig. 8K). However, we found 100% mutational frequency in one AA variant (G50W) in the Delta variant. We also found 99% mutational frequency in one AA variant (G50N) in Alpha, two AA variants (G50N, M60I) in Lambda, and one AA variant (L52F) in Mu.

Nikolaidis et al. analyzed the amino acid substitutions (AAS) and found a high rate in the spike of the Omicron [60]. Zhang et al. also evaluated AA mutations in different circulating VOCs, including Omicron. They also illustrated cross-species tropism in cells expressing 18 ACE2 molecules. This study may help to forecast the spread of mutations [61].

Heat map-like structure of mutation prevalence with > 75% mutational frequency in Omicron and comparison with the mutations with other VOCs and VOIs

Scientists have evaluated and illustrated the SARS-CoV-2 mutations, their occurrence frequencies, and their circumstances [62,63,64]. This will assist the researchers in understanding the role of this variant in epidemiology.

We analyzed the mutation prevalence of Omicron, compared the mutations with other VOCs and VOIs, and represented them using a heat map. The heat map shows eight mutations with > 90% identity (K856R, S2083I, DEL2084/2084, A2710T, T3255I, P3395H, DEL3674/3676, and I3758V) in ORF1a in Omicron (Fig. 9A). Similarly, the heat map shows one mutation with > 90% (P314L) and one with > 75% (I1566V) mutation prevalence in ORF1b of Omicron (Fig. 9B). Delta had three mutations (P314L, G662S, P1000L) with > 90% and one mutation (A1918V) with > 80% mutation prevalence. Interestingly, the heat map showed 29 mutations with a > 75% mutation prevalence in the S protein (Fig. 9C). No mutations with > 75% mutation prevalence were found in ORF3a in Omicron (Fig. 9D). Simultaneously, we found some mutations (Delta with S26L, Beta with Q57H, and S171L, Gamma with S253P, Mu with Q57H, and Del 256/257) with > 75% mutation prevalence in ORF3a with other VOCs and VOIs. We found one mutation (T9I) with a > 75% mutation prevalence in Omicron in the E protein and compared it with other VOCs and VOIs (Fig. 9E). We found one mutation (P71L) with a > 75% mutation prevalence in Beta. The heat map of the M protein showed that Omicron had three mutations (D3G, Q19E, and A63T) (Fig. 10A). ORF7a, with a > 75% mutation prevalence, was noted in Omicron. However, Delta had two mutation prevalence rates of > 75% (V82A and T120I) (Fig. 10B). ORF7b, with no mutation with > 75% prevalence, was noted in Omicron. However, Delta had a prevalence of > 75%, corresponding to T40I (Fig. 10C). We observed that the prevalence of S84L was > 75% for all ORF8 variants. The only mutation prevalence observed for Omicron with > 75% (Fig. 10D). Finally, we noted that the prevalence of the four Omicron mutations, P13L, DEL31/33, R203K, G204R was > 75% in N (Fig. 10E). We also noted four mutations with a prevalence of > 75% in Delta, Alpha, and Lambda. Characteristic mutations for Omicron and comparison with the mutations of other VOCs and VOIs were noted as deletions or nonsynonymous substitutions, which have taken place in > 75% of sequences within those variants. In this study, we measured the mutations of Omicron with > 75%. Simultaneously, this study assisted in understanding the mutations of VOCs and VOIs with > 75% (Alpha, Beta, Gamma, Delta, Lambda, and Mu). From the heat map, we found that AA variants with high mutation prevalence showed eight mutations with > 90% prevalence in ORF1a and 29 mutations with > 75% prevalence in the S-glycoprotein of Omicron. Our study is significant in this regard.

Fig. 9

The heat map-like representation shows the high mutation prevalence (> 75% mutational frequency) of Omicron and other VOCs and VOIs in ORF1a, ORF1b, S-glycoprotein, ORF3a, E. (A) Heat map of ORF1a. (B) Heat map of ORF1b. (C) Heat map of S-glycoprotein. (D) Heat map of ORF3a. (E) Heat map of E. Here, we have used outbreak.info [51] to provide the heat map like representation of high mutation (AA mutation) prevalence (> 75% mutational frequency) of Omicron and other VOCs and VOIs of different parts of the genome. The server used GISAID API data and integrated the EpiData pipeline [65] from Johns Hopkins University if needed and finally represented through a graphical interface

Fig. 10

The heat map-like representation shows the high mutation prevalence (> 75% mutational frequency) of Omicron and other VOCs and VOIs in M, ORF7a, ORF7b, ORF8, N. (A) Heat map of M. (B) Heat map of ORF7a. (C) Heat map of ORF7b. (D) Heat map of ORF8. (E) Heat map of N. Here, we have also used outbreak.info [51] to provide the heat map-like representation of high mutation (AA mutation) prevalence (> 75% mutational frequency) of Omicron and other VOCs and VOIs. The server used GISAID API data and integrated the EpiData pipeline [65] from Johns Hopkins University if needed and finally represented through a graphical interface

Scatter plot for cluster evaluation of Omicron and other VOCs and VOIs

Cluster analysis was performed, and a scatter plot was generated for all current VOCs and VOIs, in conjunction with the Omicron variant. The scatter plot shows the prevalence of all VOCs and VOIs with Omicron and indicates the origin and distribution of all current VOCs and VOIs. A scatter plot with linear regression was developed using the current circulating VOCs and VOIs (Fig. 11A). The model informs us that all the sample values were placed on both sides of the regression line. The figure shows a strong, positive, and linear scatter plot. Simultaneously, the scatter plot of the Omicron variant shows the prevalence of all samples on the upper side of the regression line (Fig. 11B).

Fig. 11

Scatter plot for cluster evaluation of Omicron and all VOCs and VOIs. (A) Scatter plot for cluster evaluation of all VOCs and VOIs. (B) Scatter plot for cluster evaluation of Omicron. The scatter plot was developed for Omicron and all VOCs and VOIs using the Nextstrain server [49,