Table 1.
Fifteen key genes of the SARS-CoV-2 genome and their locations in the reference genome of SARS-CoV-2 isolate Wuhan-Hu-1 [40].
Table 2.
Table showing the counts of genomes before and after filtering based on for Somalia, Bhutan, Hungary, Iran, and Nepal data sets.
Fig 1.
Pipeline for generating edit-distance matrix using pairwise edit distances between the variant genomes.
Fig 2.
The whole pipeline of building VEG.
The edit distance computation in this pipeline is separately shown in Fig 1.
Fig 3.
(a) The set of six variant genomes, A, B, C, D, E, and F, and their corresponding collection dates. (b) The workflow of Algorithm 1 on the distance matrix, M.
Fig 4.
Fig 5.
Count of genomes filtered based on the percentage of Ns, , in the genomes.
The x-axis shows the threshold values and the y-axis shows the count of filtered genomes. The plots are of (a) Somalia, (b) Bhutan, (c) Iran, and (d) Nepal data sets.
Fig 6.
Average count of Ns in the genome sequences vs the coding regions in five data sets.
Fig 7.
(a) (b)
, and (c)
of Bhutan data set (graph viewed using Cytoscape 3.10.2).
Fig 8.
A maximum likelihood phylogenetic tree of the Bhutan data set.
Table 3.
Benchmarking the runtime of both approaches: VEG (edit distance, pyani, sourmash) and phylogenetic tree.
Fig 9.
Venn diagrams showing parent-child relationships among the VEGs derived from sourmash, pyani, and edit distance.
(a) Bhutan, (b) Hungary, (c) Nepal, and (d) Iran data sets.
Fig 10.
The DTN is inferred from the VEG of the Bhutan data set (edit distance).
Here, the nodes are the hosts, and the edges represent the direction and day differences of the inferred transmissions.
Table 4.
Statistics showing the degree distribution of the vertices in disease transmission networks of Bhutan, Hungary, Iran, and Nepal.
Table 5.
Percentages of the count of out-degrees in the higher-degree sets of Bhutan, Hungary, Iran, and Nepal data sets.