Fig 1.
The number of clusters via two different clustering methods.
Reads of each isolate were grouped with (A) VSEARCH and (B) UMAP-HDBSCAN and calmodulin and ITS sequencing are shown in different shape and color.
Fig 2.
Distribution of identification pattern of clusters projected from different clustering methods.
Rate of clusters identified as S. schenckii sensu stricto (%) using (A) VSEARCH or (B) UMAP-HDBSCAN using calmodulin or ITS sequences. Calmodulin and ITS are shown with different shapes, each isolate with a different color.
Fig 3.
Identification of selected representative sequence from each method with calmodulin and ITS.
Isolates with representative sequences from calmodulin(A) and ITS (B) that were identified as S. schenckii sensu stricto were marked in pink and isolates only identified to the Sporothrix genus level were marked in magenta.
Fig 4.
Bootstrap consensus tree of calmodulin sequences of Sporothrix isolates.
The tree was generated using the maximum likelihood (ML) method with 1,000 bootstrap replicates with K2P+G distance model, representative sequence from polished ONT reads. The tested isolates in this study were highlighted in light grey in the format of species (isolate name) and with picture depicted the host species. The isolates which have ambiguous host information were labeled “N/A” (not available). Host graphics used in the figure were obtained as followed: cat (https://openclipart.org/detail/308557/cat-icon-grey), dog (https://openclipart.org/detail/8499/dog-simple-drawing), human (https://openclipart.org/detail/182185/man-shape) and rose (https://openclipart.org/detail/221383/rose-silhouette) and were released under the Creative Commons Zero 1.0 Public Domain License (CC0 1.0).
Table 1.
Metadata and antifungal susceptibility profiles of isolates.