Carbo Ellen C, Sidorov Igor A, van Rijn-Klink Anneloes L, Pappas Nikos, van Boheemen Sander, Mei Hailiang, Hiemstra Pieter S, Eagan Tomas M, Claas Eric C J, Kroes Aloys C M, de Vries Jutte J C
Department of Medical Microbiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands.
Sequencing Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands.
Pathogens. 2022 Mar 11;11(3):340. doi: 10.3390/pathogens11030340.
Viral metagenomics is increasingly applied in clinical diagnostic settings for detection of pathogenic viruses. While several benchmarking studies have been published on the use of metagenomic classifiers for abundance and diversity profiling of bacterial populations, studies on the comparative performance of the classifiers for virus pathogen detection are scarce. In this study, metagenomic data sets ( = 88) from a clinical cohort of patients with respiratory complaints were used for comparison of the performance of five taxonomic classifiers: Centrifuge, Clark, Kaiju, Kraken2, and Genome Detective. A total of 1144 positive and negative PCR results for a total of 13 respiratory viruses were used as gold standard. Sensitivity and specificity of these classifiers ranged from 83 to 100% and 90 to 99%, respectively, and was dependent on the classification level and data pre-processing. Exclusion of human reads generally resulted in increased specificity. Normalization of read counts for genome length resulted in a minor effect on overall performance, however it negatively affected the detection of targets with read counts around detection level. Correlation of sequence read counts with PCR Ct-values varied per classifier, data pre-processing (R range 15.1-63.4%), and per virus, with outliers up to 3 log reads magnitude beyond the predicted read count for viruses with high sequence diversity. In this benchmarking study, sensitivity and specificity were within the ranges of use for diagnostic practice when the cut-off for defining a positive result was considered per classifier.
病毒宏基因组学越来越多地应用于临床诊断环境中以检测致病病毒。虽然已经发表了几项关于使用宏基因组分类器对细菌群体进行丰度和多样性分析的基准研究，但关于分类器在病毒病原体检测方面的比较性能研究却很少。在本研究中，来自有呼吸道症状患者临床队列的宏基因组数据集（n = 88）被用于比较五种分类器的性能：Centrifuge、Clark、Kaiju、Kraken2和Genome Detective。总共13种呼吸道病毒的1144个阳性和阴性PCR结果被用作金标准。这些分类器的敏感性和特异性分别在83%至100%和90%至99%之间，并且取决于分类水平和数据预处理。排除人类读数通常会提高特异性。对基因组长度的读数计数进行归一化对整体性能影响较小，然而它对读数计数接近检测水平的目标检测有负面影响。每个分类器、数据预处理（R范围为15.1 - 63.4%）以及每种病毒的序列读数计数与PCR Ct值的相关性各不相同，对于具有高序列多样性的病毒，异常值比预测读数计数高出3个对数读数量级。在这项基准研究中，当为每个分类器考虑定义阳性结果的临界值时，敏感性和特异性在诊断实践的使用范围内。
