Figure 1 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Correlation between sequencing and RT-qPCR results for PC. The number of reads assigned to each dilution of amplicon 96 of the PC and their Ct is reported. Linear regression lines and confidence intervals are also shown.
Source publication
Diagnostic tests based on reverse transcription–quantitative polymerase chain reaction (RT–qPCR) are the gold standard approach to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from clinical specimens. However, unless specifically optimized, this method is usually unable to recognize the specific viral strain respons...
Contexts in source publication
Context 1
... were initially analysed. Increasing amounts of sequencing reads were assigned to each PC dilution (Table 3). A statistically significant correlation was observed between the logarithmic number of generated sequencing reads, ranging from 5 to 10,668, and the Ct of the starting amplicon copy number (Pearson r ¼ À0.864, t test, P ¼ 0.005) (Fig. 1). When sequencing libraries were directly generated from 10 5 or 10 3 amplicons, the number of reads assigned to SARS-CoV-2 genome was neither consistently above the NTC sample nor proportional to the theoretical input amplicon copies. Conversely, when introducing 10 7 amplicon copies in library preparation, the number of reads assigned ...
Context 2
... the low number of reads, STArS detected SARS-CoV-2 in two out of three clinical samples, in agreement with RT-qPCR results and confirming the reliability of the previously defined scoring rule (Table 4). In terms of viral genotyping, sample '10' was classified as B.1.1.28.7 strain, while sample '09' could not be genotyped due to the low sequencing read coverage (Supplementary Fig. S1). ...
Similar publications
The emergence of a highly contagious novel coronavirus in 2019 led to an unprecedented need for large scale diagnostic testing. The associated challenges including reagent shortages, cost, deployment delays, and turnaround time have all highlighted the need for an alternative suite of low-cost tests. Here, we demonstrate a diagnostic test for SARS-...
Citations
... Nanopore platform classified 10 out of 14 samples as Apicomplexa positive, according to a scoring rule we developed, which classifies a sample as Apicomplexa positive in case the number of reads assigned to Apicomplexa is at least 5-fold the average number of reads assigned to Apicomplexa for negative controls. This scoring rule was adapted from previous works describing the adoption of Nanopore sequencing for pathogen detection [29,30], while the Illumina platform classified all 14 samples as Apicomplexa positive. In particular, the four samples classified as Apicomplexa negative by the Nanopore platform had a very low percentage of reads assigned to Apicomplexa also in the Illumina analysis, namely 0.22%, 0.09%, 0.04%, and 0.02% for the G0159P, G0173CR, G0173L, and G0225CR1 samples, respectively. ...
French Guiana, located in the Guiana Shield, is a natural reservoir for many zoonotic pathogens that are of considerable medical or veterinary importance. Until now, there has been limited data available on the description of parasites circulating in this area, especially on protozoan belonging to the phylum Apicomplexa; conversely, the neighbouring countries describe a high parasitic prevalence in animals and humans. Epidemiological surveillance is necessary, as new potentially virulent strains may emerge from these forest ecosystems, such as Amazonian toxoplasmosis. However, there is no standard tool for detecting protozoa in wildlife. In this study, we developed Meat-Borne-Parasite, a high-throughput meta-barcoding workflow for detecting Apicomplexa based on the Oxford Nanopore Technologies sequencing platform using the 18S gene of 14 Apicomplexa positive samples collected in French Guiana. Sequencing reads were then analysed with MetONTIIME pipeline. Thanks to a scoring rule, we were able to classify 10 samples out of 14 as Apicomplexa positive and reveal the presence of co-carriages. The same samples were also sequenced with the Illumina platform for validation purposes. For samples identified as Apicomplexa positive by both platforms, a strong positive correlation at up to the genus level was reported. Overall, the presented workflow represents a reliable method for Apicomplexa detection, which may pave the way for more comprehensive biomonitoring of zoonotic pathogens.
