Research and medical genomics require comprehensive, scalable methods for the discovery of novel disease targets, evolutionary drivers and genetic markers with clinical significance. This necessitates a framework to identify all types of variants independent of their size or location. Here we present DRAGEN, which uses multigenome mapping with pangenome references, hardware acceleration and machine learning-based variant detection to provide insights into individual genomes, with ~30 min of computation time from raw reads to variant detection. DRAGEN outperforms current state-of-the-art methods in speed and accuracy across all variant types (single-nucleotide variations, insertions or deletions, short tandem repeats, structural variations and copy number variations) and incorporates specialized methods for analysis of medically relevant genes. We demonstrate the performance of DRAGEN across 3,202 whole-genome sequencing datasets by generating fully genotyped multisample variant call format files and demonstrate its scalability, accuracy and innovation to further advance the integration of comprehensive genomics. Overall, DRAGEN marks a major milestone in sequencing data analysis and will provide insights across various diseases, including Mendelian and rare diseases, with a highly comprehensive and scalable platform.

研究和医学基因组学需要全面、可扩展的方法来发现具有临床意义的新疾病靶点、进化驱动因素和遗传标记物。这需要一个框架来识别所有类型的变体，而不受其大小或位置的影响。在这里，我们介绍了 DRAGEN，它使用带有泛基因组参考的多基因组映射、硬件加速和基于机器学习的变异检测来提供对单个基因组的见解，从原始读取到变异检测的计算时间为 ~30 分钟。DRAGEN 在所有变体类型（单核苷酸变异、插入或缺失、短串联重复序列、结构变异和拷贝数变异）的速度和准确性方面都优于当前最先进的方法，并结合了分析医学相关基因的专用方法。我们通过生成完全基因分型的多样本变异检出格式文件，展示了 DRAGEN 在 3,202 个全基因组测序数据集中的性能，并展示了其可扩展性、准确性和创新性，以进一步推进综合基因组学的整合。总体而言，DRAGEN 标志着测序数据分析的一个重要里程碑，并将通过高度全面和可扩展的平台提供对各种疾病的见解，包括孟德尔和罕见病。