ArticleBMC biology2024
A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset.
Article in BMC biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- Augmented CENH3 loading is accompanied by transcriptional and epigenetic reprogramming at rice centromeres during meiosis.Genome biology · 2026Article
- Fungal photobiont and microbiome genome composition in the Cladonia uncialis tripartite symbiosis.Scientific data · 2026Article
- First report of whole genome sequence of a Mycobacterium bovis (3/86Rv) strain isolated from a cow in India.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2025Article
- Low-call-rate SNPs and presence-absence variation identified in the rice pan-genome can improve genomic prediction of rice gene bank accessions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- A 17.1 kb duplication downstream GATA6 is strongly associated with egg weight in chicken.BMC genomics · 2025Article
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- Genome-wide association studies and genomic selection for leaf-related traits in maize.Frontiers in plant science · 2025Article
- Protein truncating splice site variant inInternational journal of ophthalmology · 2025Article
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19 authors.
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Abstract
backgroundSingle-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.
resultsHere 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).
conclusionsThis 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.
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