A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset

Abstract
Background Single-nucleotide polymorphisms (SNPs) are the most widely used form of molecular genetic variation studies. As reference genomes and resequencing data sets expand exponentially, tools must be in place to call SNPs at a similar pace. The genome analysis toolkit (GATK) is one of the most widely used SNP calling software tools publicly available, but unfortunately, high-performance computing versions of this tool have yet to become widely available and affordable.

            Results
            Here we report an open-source high-performance computing genome variant calling workflow (HPC-GVCW) for GATK that can run on multiple computing platforms from supercomputers to desktop machines. We benchmarked HPC-GVCW on multiple crop species for performance and accuracy with comparable results with previously published reports (using GATK alone). Finally, we used HPC-GVCW in production mode to call SNPs on a “subpopulation aware” 16-genome rice reference panel with ~ 3000 resequenced rice accessions. The entire process took ~ 16 weeks and resulted in the identification of an average of 27.3 M SNPs/genome and the discovery of ~ 2.3 million novel SNPs that were not present in the flagship reference genome for rice (i.e., IRGSP RefSeq).
          
            Conclusions
            This study developed an open-source pipeline (HPC-GVCW) to run GATK on HPC platforms, which significantly improved the speed at which SNPs can be called. The workflow is widely applicable as demonstrated successfully for four major crop species with genomes ranging in size from 400 Mb to 2.4 Gb. Using HPC-GVCW in production mode to call SNPs on a 25 multi-crop-reference genome data set produced over 1.1 billion SNPs that were publicly released for functional and breeding studies. For rice, many novel SNPs were identified and were found to reside within genes and open chromatin regions that are predicted to have functional consequences. Combined, our results demonstrate the usefulness of combining a high-performance SNP calling architecture solution with a subpopulation-aware reference genome panel for rapid SNP discovery and public deployment.<br><br><h5>Acknowledgements</h5>The authors acknowledge support from the Shaheen Cray XC40 Supercomputing and Ibex heterogeneous cluster platforms at the KAUST Supercomputing Laboratory (KSL). The authors acknowledge data availability and visualization at SNP-Seek, and Amazon Web Services (AWS) Open Data as a data repository, Gramene, Sorghumbase portals, and the KAUST Research Repository.<br>This research was supported by King Abdullah University of Science & Technology’s Baseline funding and the University of Arizona’s Bud Antle Endowed Chair for Excellent in Agriculture to R.A.W.<br><br><h5>Publisher</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.publisher=Springer Science and Business Media LLC,equals">Springer Science and Business Media LLC</a><br><br><h5>Journal</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.journal=BMC Biology,equals">BMC Biology</a><br><br><h5>DOI</h5><a href="https://doi.org/10.1186/s12915-024-01820-5">10.1186/s12915-024-01820-5</a><br><br><h5>Additional Links</h5>https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-024-01820-5</span>