The article discusses the development and implementation of a web-based method for Multilocus Sequence Typing (MLST) of bacteria using whole-genome sequencing (WGS) data. MLST is considered the "gold standard" for bacterial strain identification, but traditional methods are costly and time-consuming. With the decreasing costs of WGS, this method offers a more efficient and accessible approach. The method uses short sequence reads from various sequencing platforms or preassembled genomes to identify MLST alleles and determine sequence types (STs). The method was tested on preassembled genomes and short sequence reads from multiple isolates, covering various MLST schemes. The results showed that the method accurately identified STs for most isolates, with some minor mismatches reflecting the quality of the draft genomes. The web server is publicly available and designed to be user-friendly, making it accessible to investigators with limited bioinformatics experience. The authors also discuss the potential for future improvements, such as automatic species detection and phylogenetic analysis.The article discusses the development and implementation of a web-based method for Multilocus Sequence Typing (MLST) of bacteria using whole-genome sequencing (WGS) data. MLST is considered the "gold standard" for bacterial strain identification, but traditional methods are costly and time-consuming. With the decreasing costs of WGS, this method offers a more efficient and accessible approach. The method uses short sequence reads from various sequencing platforms or preassembled genomes to identify MLST alleles and determine sequence types (STs). The method was tested on preassembled genomes and short sequence reads from multiple isolates, covering various MLST schemes. The results showed that the method accurately identified STs for most isolates, with some minor mismatches reflecting the quality of the draft genomes. The web server is publicly available and designed to be user-friendly, making it accessible to investigators with limited bioinformatics experience. The authors also discuss the potential for future improvements, such as automatic species detection and phylogenetic analysis.