DRAGEN is a comprehensive and accurate genome analysis tool that enables the detection of all types of genomic variants, including SNVs, indels, STRs, SVs, and CNVs, in a scalable and efficient manner. The tool utilizes multigenome (graph) approaches, hardware acceleration, and machine learning-based variant detection to provide insights into individual genomes within approximately 30 minutes of computation time. DRAGEN outperforms existing state-of-the-art methods in speed and accuracy across all variant types, including SNVs, indels, STRs, SVs, and CNVs. It also incorporates specialized methods to obtain key insights in medically relevant genes, such as HLA, SMN, and GBA. DRAGEN was tested across 3,202 genomes from the 1000 Genomes Project (1kGP) and demonstrated its scalability, accuracy, and ability to provide comprehensive insights into genomic variation. The tool was able to detect a wide range of variants, including SNVs, indels, STRs, SVs, and CNVs, and generate population-level VCF files for each variant type. DRAGEN's performance was benchmarked against other variant callers, including GATK, DeepVariant, and Giraffe, and showed superior accuracy and speed in variant detection. DRAGEN's ability to detect variants in medically relevant genes, such as CYP2D6, CYP2B6, GBA, and HLA, was also demonstrated. The tool was able to identify variants that are associated with various diseases, including Parkinson's disease, Gaucher's disease, and alpha-thalassemia. DRAGEN's performance was further validated using the Genome in a Bottle (GIAB) benchmarks, which showed that it achieved high accuracy in variant detection across all variant types. Overall, DRAGEN provides a powerful and efficient solution for comprehensive genome analysis, enabling the detection of all types of genomic variants with high accuracy and speed. The tool's ability to scale to large populations and its integration of specialized methods for medically relevant genes make it a valuable resource for research and clinical applications in genomics.DRAGEN is a comprehensive and accurate genome analysis tool that enables the detection of all types of genomic variants, including SNVs, indels, STRs, SVs, and CNVs, in a scalable and efficient manner. The tool utilizes multigenome (graph) approaches, hardware acceleration, and machine learning-based variant detection to provide insights into individual genomes within approximately 30 minutes of computation time. DRAGEN outperforms existing state-of-the-art methods in speed and accuracy across all variant types, including SNVs, indels, STRs, SVs, and CNVs. It also incorporates specialized methods to obtain key insights in medically relevant genes, such as HLA, SMN, and GBA. DRAGEN was tested across 3,202 genomes from the 1000 Genomes Project (1kGP) and demonstrated its scalability, accuracy, and ability to provide comprehensive insights into genomic variation. The tool was able to detect a wide range of variants, including SNVs, indels, STRs, SVs, and CNVs, and generate population-level VCF files for each variant type. DRAGEN's performance was benchmarked against other variant callers, including GATK, DeepVariant, and Giraffe, and showed superior accuracy and speed in variant detection. DRAGEN's ability to detect variants in medically relevant genes, such as CYP2D6, CYP2B6, GBA, and HLA, was also demonstrated. The tool was able to identify variants that are associated with various diseases, including Parkinson's disease, Gaucher's disease, and alpha-thalassemia. DRAGEN's performance was further validated using the Genome in a Bottle (GIAB) benchmarks, which showed that it achieved high accuracy in variant detection across all variant types. Overall, DRAGEN provides a powerful and efficient solution for comprehensive genome analysis, enabling the detection of all types of genomic variants with high accuracy and speed. The tool's ability to scale to large populations and its integration of specialized methods for medically relevant genes make it a valuable resource for research and clinical applications in genomics.