Metagenomics is a powerful tool to identify novel or unexpected pathogens, since it is generic and relatively unbiased. The limit of detection (LOD) is a critical parameter for the routine application of methods in the clinical diagnostic context. Although attempts for the determination of LODs for metagenomics next-generation sequencing (mNGS) have been made previously, these were only applicable for specific target species in defined samples matrices. Therefore, we developed and validated a generalized probability-based model to assess the sample-specific LOD of mNGS experiments (LOD). Initial rarefaction analyses with datasets of Borna disease virus 1 human encephalitis cases revealed a stochastic behavior of virus read detection. Based on this, we transformed the Bernoulli formula to predict the minimal necessary dataset size to detect one virus read with a probability of 99%. We validated the formula with 30 datasets from diseased individuals, resulting in an accuracy of 99.1% and an average of 4.5 ± 0.4 viral reads found in the calculated minimal dataset size. We demonstrated by modeling the virus genome size, virus-, and total RNA-concentration that the main determinant of mNGS sensitivity is the virus-sample background ratio. The predicted LOD for the respective pathogenic virus in the datasets were congruent with the virus-concentration determined by RT-qPCR. Theoretical assumptions were further confirmed by correlation analysis of mNGS and RT-qPCR data from the samples of the analyzed datasets. This approach should guide standardization of mNGS application, due to the generalized concept of LOD.

中文翻译：

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评估临床宏基因组学样本特异性检测限的理论和通用方法


宏基因组学是识别新型或意外病原体的强大工具，因为它是通用的且相对公正。检测限 (LOD) 是临床诊断背景下常规应用方法的关键参数。尽管之前已经尝试过确定宏基因组学下一代测序 (mNGS) 的 LOD，但这些仅适用于定义的样品基质中的特定目标物种。因此，我们开发并验证了一个基于概率的广义模型来评估 mNGS 实验 (LOD) 的样本特定 LOD。对博尔纳病病毒 1 人类脑炎病例数据集的初步稀疏分析揭示了病毒读取检测的随机行为。在此基础上，我们对伯努利公式进行了改造，以预测以 99% 的概率检测到一种病毒所需的最小数据集大小。我们使用来自患病个体的 30 个数据集验证了该公式，结果准确度为 99.1%，在计算的最小数据集大小中发现平均 4.5 ± 0.4 个病毒读数。我们通过对病毒基因组大小、病毒和总 RNA 浓度进行建模证明，mNGS 敏感性的主要决定因素是病毒样本背景比。数据集中各致病病毒的预测 LOD 与 RT-qPCR 测定的病毒浓度一致。通过对分析数据集样本中的 mNGS 和 RT-qPCR 数据进行相关性分析，进一步证实了理论假设。由于 LOD 的广义概念，这种方法应该指导 mNGS 应用的标准化。