Personalized medicine tailors treatments based on the individual characteristics of each patient. Recent work targets comprehensive analysis of patient samples that contain human, viral, and bacterial genomes. Novel technologies like Simul-seq enable simultaneous analysis of such samples. However, existing workflows are slow and disjoint. In this paper, we present FPGA accelerated genomics infrastructure, GenoMiX , that supports multiple real-world analysis pipelines used in personalized medicine, including phylogenetic assignment for viral pathogens, variant calling that is key for cancer genomics, and microbiome metagenome analysis. We integrate our workflow with Qiita, an open-source framework for managing and analyzing multi-omics datasets. GenoMiX is not only up to 30× faster than comparable CPU-based tools, but it also addresses the challenges associated with handling reference databases of varying sizes, encompassing viruses, human genomes, and microbiomes. Our optimized infrastructure was a key component enabling success of the award-winning UCSD’s “Return to Learn” program during the COVID-19 public health emergency in San Diego County.