Typing of group A Streptococcus (GAS) is crucial for infection control and epidemiology. While whole-genome sequencing (WGS) is revolutionizing the way that bacterial organisms are typed, it is necessary to provide backward compatibility with currently used typing schemas to facilitate comparisons and understanding of epidemiological trends. Here, we sequenced the genomes of 191 GAS isolates representing 42 different emm types and used bioinformatics tools to derive commonly used GAS typing information directly from the short-read WGS data. We show that emm typing and multilocus sequence typing can be achieved rapidly and efficiently using this approach, which also permits the determination of the presence or absence of genes associated with GAS tissue tropism. We also report on how the WGS data analysis was instrumental in identifying ambiguities present in the commonly used emm type database hosted by the U.S. Centers for Disease Control and Prevention.
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