Methicillin-resistant Staphylococcus aureus (MRSA) poses a significant public threat in hospitals, impacting patient lives and creating a financial burden. In recent years, both community-associated (CA) and healthcare- associated (HA) MRSA strain types have been seen in various U.S. hospitals, often co-existing in the same ward or ICU location, complicating the efforts of infection prevention and control. While Whole Genome Sequencing (WGS) has started to show significant promise for identifying emerging strain clusters, S. aureus poses unique challenges for genomic epidemiology. Due to the hyper-mutation rates of some S. aureus clades, samples within an outbreak cluster can differ by as many as 25-40 SNPs, a far higher threshold than other bacterial pathogens. Additionally, isolates from the same colony often display high genomic diversity, especially when the samples are taken from nasal swabs. This genomic diversity within a single colony further complicates the difficulty of identifying emerging clusters as part of routine infection prevention and transmission epidemiology. In this proposal, we look to develop an automated cloud-based bioinformatics platform that is designed to address these unique challenges in analyzing WGS data from MRSA isolates. Our platform will avoid traditional core-alignment methodologies to separate genomic clusters and instead use a different methodology to group isolates that share recent phylogeny. In a preliminary evaluation, we performed a manual analysis of this new approach by using raw sequence data from a recent MRSA study at an ICU setting. Besides correctly separating strain clusters from the surrounding isolates, our platform was able to additionally isolate critical biomarkers that core-alignment methodologies are not designed for. Our cloud-based MRSA genomics framework will be implemented in Amazon Web Services (AWS) cloud. Following implementation, evaluation will be carried out using both public NCBI data and MRSA sequence data from a single hospital. Our Phase 1 benchmark will be to complete all WGS analyses for 100 isolates within one hour. Our Phase 1 aims are: 1) Develop a WGS-based screening module that accurately identifies MRSA clonal complexes; 2.) Develop a WGS-based clonal module that further separates clonal MRSA isolates into distinct lineages and underlying clusters; 3.) Evaluate both screening and clonal modules by sequencing 100 banked samples of CA-MRSA and HA-MRSA isolates from a single U.S. hospital.