Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
Purdue Institute for Inflammation, Immunology, & Infectious Disease, Purdue University, West Lafayette, IN, USA.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac314.
As recently demonstrated by the COVID-19 pandemic, large-scale pathogen genomic data are crucial to characterize transmission patterns of human infectious diseases. Yet, current methods to process raw sequence data into analysis-ready variants remain slow to scale, hampering rapid surveillance efforts and epidemiological investigations for disease control. Here, we introduce an accelerated, scalable, reproducible, and cost-effective framework for pathogen genomic variant identification and present an evaluation of its performance and accuracy across benchmark datasets of Plasmodium falciparum malaria genomes. We demonstrate superior performance of the GPU framework relative to standard pipelines with mean execution time and computational costs reduced by 27× and 4.6×, respectively, while delivering 99.9% accuracy at enhanced reproducibility.
正如最近的 COVID-19 大流行所表明的那样，大规模病原体基因组数据对于描述人类传染病的传播模式至关重要。然而，将原始测序数据处理为可分析的变异体的当前方法在规模上仍然很慢，阻碍了疾病控制的快速监测工作和流行病学调查。在这里，我们引入了一种加速的、可扩展的、可重复的和具有成本效益的病原体基因组变异体识别框架，并在恶性疟原虫疟疾基因组的基准数据集上评估了其性能和准确性。我们证明了 GPU 框架相对于标准管道的优越性能，平均执行时间和计算成本分别降低了 27 倍和 4.6 倍，同时在增强的可重复性下实现了 99.9%的准确率。
