Ebinger A, Fischer S, Höper D. A theoretical and generalized approach for the assessment of the sample-specific limit of detection for clinical metagenomics.
Comput Struct Biotechnol J 2020;
19:732-742. [PMID:
33552445 PMCID:
PMC7822954 DOI:
10.1016/j.csbj.2020.12.040]
[Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/16/2020] [Accepted: 12/24/2020] [Indexed: 12/18/2022] Open
Abstract
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 (LODmNGS). 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 LODmNGS 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 LODmNGS.
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