Benchmarking germline variant calling performance of a GPU-accelerated tool on whole-genome sequencing datasets

Abstract Background Rapid advances in next-generation sequencing (NGS) have enabled ultralarge population and cohort studies to identify DNA variants that may impact gene function. Efficient bioinformatics tools, such as read alignment and variant calling, are essential for processing massive amounts of sequencing data. To increase the analysis speed, multiple software and hardware acceleration strategies have been developed. This study comprehensively evaluated germline variant calling via the GPU-based acceleration tool BaseNumber using WGS datasets from various sources. These included standard whole-genome sequencing (WGS) data from the Genome in a Bottle (GIAB) and the Golden Standard of China Genome (GSCG) projects, resequenced GSCG samples, and 100 in-house samples from the Genome Sequencing of Rare Diseases (GSRD) project. The variant calling outputs were compared to the reference and the results generated by the Burrows-Wheeler Aligner (BWA) and Genome Analysis Toolkit (GATK) pipeline. Results BaseNumber demonstrated high precision (99.32%) and recall (99.86%) rates in variant calls compared to the standard reference. The output comparison between the BaseNumber and GATK pipelines yielded nearly identical results, with a mean F1 score of 99.69%. Additionally, BaseNumber took 23 minutes on average to analyze a 48X WGS sample, which was 215.33 times faster than the GATK workflow. Conclusions The GPU-based BaseNumber provides a highly accurate and ultrafast variant calling capability, significantly improving WGS analysis efficiency and facilitating time-sensitive tests, such as clinical WGS genetic diagnosis. This study also sheds light on the GPU-based acceleration of other omics data analyses.