A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset
Yong Zhou,
Nagarajan Kathiresan,
Zhichao Yu,
Luis F. Rivera,
Yujian Yang,
Manjula Thimma,
Keerthana Manickam,
Dmytro Chebotarov,
Ramil Mauleon,
Kapeel Chougule,
Sharon Wei,
Tingting Gao,
Carl D. Green,
Andrea Zuccolo,
Weibo Xie,
Doreen Ware,
Jianwei Zhang,
Kenneth L. McNally,
Rod A. Wing
Affiliations
Yong Zhou
Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST)
Nagarajan Kathiresan
KAUST Supercomputing Laboratory (KSL), King Abdullah University of Science and Technology (KAUST)
Zhichao Yu
Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST)
Luis F. Rivera
Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST)
Yujian Yang
National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University
Manjula Thimma
Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST)
Keerthana Manickam
Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST)
Dmytro Chebotarov
International Rice Research Institute (IRRI)
Ramil Mauleon
International Rice Research Institute (IRRI)
Kapeel Chougule
Cold Spring Harbor Laboratory
Sharon Wei
Cold Spring Harbor Laboratory
Tingting Gao
National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University
Carl D. Green
Information Technology Department, King Abdullah University of Science and Technology (KAUST)
Andrea Zuccolo
Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST)
Weibo Xie
National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University
Doreen Ware
Cold Spring Harbor Laboratory
Jianwei Zhang
National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University
Kenneth L. McNally
International Rice Research Institute (IRRI)
Rod A. Wing
Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST)
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.
