Joint processing of long- and short-read sequencing data with deep learning improves variant calling.

Despite the complementary strengths of short- and long-read sequencing approaches, variant calling methods still rely on a single data type. In this study, we collected and harmonized Nanopore dataset of the 7 GIAB healthy individuals across three independent consortia. Then, by leveraging these harmonized Nanopore data, we explore the benefits of using a hybrid DeepVariant model to jointly process Illumina and Nanopore data for germline variant detection. We show that a shallow hybrid long-short sequencing approach can match or surpass germline variant detection accuracy of state-of-the-art single-technology methods, potentially reducing overall sequencing costs and with the advantage of also enabling detection of large germline structural variations. These findings offer promising potential for molecular diagnostics in clinical settings, particularly for rare genetic disease screenings.