The paper introduces DRAGEN, a comprehensive and scalable solution for genome analysis that can identify all types of variants (SNVs, indels, structural variations, CNVs, and repeat expansions) with high accuracy and speed. DRAGEN leverages multigenome (graph) references, hardware acceleration, and machine learning to detect variants from raw reads to variant detection in approximately 30 minutes. The method outperforms existing state-of-the-art tools in terms of speed and accuracy across all variant types. DRAGEN also includes specialized methods for medically relevant genes, such as HLA, SMN, and GBA. The authors demonstrate the scalability and accuracy of DRAGEN by analyzing 3,202 whole-genome samples from the 1000 Genomes Project (1kGP) cohort, showing its ability to capture the entire spectrum of genomic variations at scale. The results highlight the utility of DRAGEN in advancing research and medical applications in genomics.The paper introduces DRAGEN, a comprehensive and scalable solution for genome analysis that can identify all types of variants (SNVs, indels, structural variations, CNVs, and repeat expansions) with high accuracy and speed. DRAGEN leverages multigenome (graph) references, hardware acceleration, and machine learning to detect variants from raw reads to variant detection in approximately 30 minutes. The method outperforms existing state-of-the-art tools in terms of speed and accuracy across all variant types. DRAGEN also includes specialized methods for medically relevant genes, such as HLA, SMN, and GBA. The authors demonstrate the scalability and accuracy of DRAGEN by analyzing 3,202 whole-genome samples from the 1000 Genomes Project (1kGP) cohort, showing its ability to capture the entire spectrum of genomic variations at scale. The results highlight the utility of DRAGEN in advancing research and medical applications in genomics.