Wang Xuebin, Wang Taifu, Xie Zhihao, Zhang Youjin, Xia Shiqiang, Sun Ruixue, He Xinqiu, Xiang Ruizhi, Zheng Qiwen, Liu Zhencheng, Wang Jin'An, Wu Honglong, Jin Xiangqian, Chen Weijun, Li Dongfang, He Zengquan
BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China.
BGI PathoGenesis Pharmaceutical Technology, BGI-Shenzhen, Shenzhen 518083, China.
Brief Bioinform. 2023 Mar 19;24(2). doi: 10.1093/bib/bbad092.
Metagenomic sequencing (mNGS) is a powerful diagnostic tool to detect causative pathogens in clinical microbiological testing owing to its unbiasedness and substantially reduced costs. Rapid and accurate classification of metagenomic sequences is a critical procedure for pathogen identification in dry-lab step of mNGS test. However, clinical practices of the testing technology are hampered by the challenge of classifying sequences within a clinically relevant timeframe. Here, we present GPMeta, a novel GPU-accelerated approach to ultrarapid pathogen identification from complex mNGS data, allowing users to bypass this limitation. Using mock microbial community datasets and public real metagenomic sequencing datasets from clinical samples, we show that GPMeta has not only higher accuracy but also significantly higher speed than existing state-of-the-art tools such as Bowtie2, Bwa, Kraken2 and Centrifuge. Furthermore, GPMeta offers GPMetaC clustering algorithm, a statistical model for clustering and rescoring ambiguous alignments to improve the discrimination of highly homologous sequences from microbial genomes with average nucleotide identity >95%. GPMetaC exhibits higher precision and recall rate than others. GPMeta underlines its key role in the development of the mNGS test in infectious diseases that require rapid turnaround times. Further study will discern how to best and easily integrate GPMeta into routine clinical practices. GPMeta is freely accessible to non-commercial users at https://github.com/Bgi-LUSH/GPMeta.
宏基因组测序（mNGS）是临床微生物检测中一种强大的诊断工具，因其无偏性和成本大幅降低，可用于检测致病病原体。宏基因组序列的快速准确分类是mNGS检测湿实验室步骤中病原体鉴定的关键程序。然而，该检测技术的临床应用受到在临床相关时间范围内对序列进行分类这一挑战的阻碍。在此，我们展示了GPMeta，这是一种新型的GPU加速方法，用于从复杂的mNGS数据中超快速鉴定病原体，使用户能够绕过这一限制。使用模拟微生物群落数据集和来自临床样本的公共真实宏基因组测序数据集，我们表明GPMeta不仅具有更高的准确性，而且比现有最先进的工具（如Bowtie2、Bwa、Kraken2和Centrifuge）速度显著更快。此外，GPMeta提供了GPMetaC聚类算法，这是一种用于对模糊比对进行聚类和重新评分的统计模型，以提高对平均核苷酸同一性>95%的微生物基因组中高度同源序列的区分能力。GPMetaC的精度和召回率高于其他算法。GPMeta突显了其在需要快速周转时间的传染病mNGS检测发展中的关键作用。进一步的研究将探讨如何最好且轻松地将GPMeta整合到常规临床实践中。非商业用户可在https://github.com/Bgi-LUSH/GPMeta免费获取GPMeta。
