Candida auris, a multidrug-resistant yeast, is an opportunistic pathogen that is capable of causing invasive infections, particularly in hospitalized patients with compromised immune systems. Whole genome sequencing (WGS) analysis provides valuable information related to the presence of C. auris clades and antifungal drug resistance mutations, and the data can be used to study transmission patterns in healthcare settings. However, analyzing data can be challenging due to its massive size and complexity. The data generated from WGS require advanced computational infrastructure and expertise in bioinformatics to handle tasks such as data preprocessing, quality control, read alignment, and variant calling. The purpose of this review is to provide a comprehensive overview of the computational tools and software used for WGS data analysis of C. auris. In this review, we have summarized all the tools, software, and databases used so far to the best of our knowledge for analysis of C. auris WGS data by several scientists.
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