Ultra-fast haplotype phasing and genotype imputation service using a hybrid FPGA-GPU system

发起了大规模的国际社会项目，如美国100万志愿者健康研究，对100多万人的基因组进行排序。这种关键DNA参考数据的主要用途是允许定相和插补，即估计个人单倍型和预测全球不同临床生物库的个体全基因组数据的缺失基因型。然而，利用超过32，000个欧洲个体的最新单倍型参考群体，即使是强大的专用聚类系统，如桑格插补服务（SIS），也要运行数天。更快和更节能的计算架构需要将计算需求降低几个数量级，以便实际上允许使用不同的全球参考群体和更大的参考面板的所有新的实际好处。我们建议提供基于超快速单体型定相和基因型插补混合FPGA-GPU系统的开发的定相和插补网络服务，以实现超快速单体型定相和基因型插补。快速定相和从不同的参考人群中插补。针对现场可编程门阵列（FPGA）和图形处理单元（GPU）的混合组合，为算法设计引入了一个新的有前途的科学研究领域。此外，我们将在算法上改进SIS的HapHedge数据结构，以开发出另一种适合并行使用和FPGA结构的快速查找算法。对于基于最近的Eagle v2和PBWT工具的单倍型定相和插补，我们的悲观（即下限）预期已经显示，与16核CPU机器和桑格插补服务相比，单个FPGA处理器上的加速分别为至少164和142，从而将中等大小的全基因组数据集的运行时间从几天减少到几分钟。因此，对于分阶段和估算，我们计划为我们的服务提供的仅具有四个FPGA处理器的单个标准计算系统与用于并行计算的650多个16核CPU核心的大型HPC计算集群一样强大，同时节省了99%以上的能源，甚至还没有考虑我们计划的GPU集成。我们建议实施并向研究界提供一个可自由访问的图形决策网络界面。我们已经设计了一个“对话界面”原型，可以从不同的参考面板集合中快速轻松地进行定相和插补，并在运行时对定相和插补精度进行“即时”估计。基于FPGA-GPU的混合定相和插补服务将为应用遗传研究开辟全新的可能性，例如，在来自全球不同参考人群的插补面板之间切换，以最终最大限度地提高个体水平上的定相和插补准确性-这是个性化医疗的重要先决条件。

Large-scale international community projects such as the United States 1-million-volunteer health study were launched to sequence the genomes of more than one million individuals. A major use of this key DNA reference data is to allow phasing and imputation, i.e. estimating personal haplotypes and predicting missing genotypes of individual genome-wide data of diverse clinical biobanks worldwide. However, utilizing the most recent haplotype reference population of more than 32,000 European individuals keep even powerful dedicated cluster systems, such as the Sanger Imputation Service (SIS), running for days. Faster and more energy-efficient computational architectures are required to reduce the computational requirements by several orders of magnitude in order to actually allow for all new practical benefits from using diverse sets of worldwide reference populations and larger reference panels.We propose to provide a phasing and imputation webservice based on the development of an ultra-fast haplotype phasing and genotype imputation hybrid FPGA-GPU system to enable ultra-fast phasing and imputation from diverse sets of reference populations. Targeting a hybrid composition of Field-Programmable Gate Arrays (FPGAs) and Graphical Processing Units (GPUs) introduces a new promising field of scientific research for algorithmic design. Besides, we will algorithmically improve the HapHedge data structure of the SIS to develop another fast lookup algorithm perfectly suited to parallel use and FPGA structure. For haplotype phasing and imputation based on the recent Eagle v2 and PBWT tools our pessimistic, i.e. lower bound, expectations already show a speedup of at least 164 and 142 on one single FPGA processor as compared to a 16-core CPU machine and the Sanger Imputation Service, respectively, thereby reducing the runtime for a medium-size genome-wide data set from days to minutes. Thus, for phasing and imputation, a single standard computing system with only four FPGA processors, as we plan to provide for our service, is as powerful as a large HPC computing cluster of more than 650 16-core CPU cores for parallel computing, while saving more than 99% of energy, and that does not even take our planned GPU integration into account.Further, we propose to implement and provide the research community with a freely accessible graphical decision-making web-interface. We already designed a "conversational interface" prototype to enable quick and easy phasing and imputation from diverse sets of reference panels in conjunction with an "on-the-fly" estimation of phasing and imputation accuracy during runtime. The hybrid FPGA-GPU-based phasing and imputation service will open up entirely new possibilities for applied genetic research, e.g. toggling between imputation panels from diverse sets of worldwide reference populations, to ultimately maximize phasing and imputation accuracy on the individual level - an important prerequisite for personalized medicine.