The Swedish Maternal Microbiome (SweMaMi) study is a large, population-based longitudinal cohort designed to investigate the role of the maternal microbiome in pregnancy outcomes and maternal health. The cohort includes thousands of pregnant participants recruited across Sweden, with biological samples collected at multiple timepoints during pregnancy alongside detailed questionnaire data and linkage to national health registries. These resources enable the study of microbial, clinical, and environmental factors influencing maternal and neonatal outcomes. High-throughput sequencing of maternal samples, including vaginal and faecal microbiomes, generates large-scale metagenomic datasets requiring substantial computational resources for processing and analysis. Bioinformatic workflows include quality control, host read removal, taxonomic and functional profiling, microbial community analysis, and integration with clinical metadata and registry variables. Downstream analyses involve advanced statistical modelling and machine learning approaches to investigate associations between microbial community structure and pregnancy outcomes such as spontaneous preterm birth. The project relies on scalable high-performance computing to process sequencing datasets, manage large multi-modal data structures, and implement reproducible analysis pipelines. Access to UPPMAX computational infrastructure will enable efficient execution of metagenomic processing workflows, large-scale statistical analyses, and integration of microbiome data with clinical and registry-derived variables, ultimately supporting a deeper understanding of microbiome contributions to maternal and neonatal health