Figure 4 - available via license: Creative Commons Attribution 4.0 International
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Identification of SARS-CoV-2 strains using STArS. The heatmap represents the distribution of a subset of mutations of interest (rows) across the samples (columns). Blue stands for reference genotype, red stands for variant genotype, while grey stands for not genotypable position. Sample '172' was excluded from the heatmap, since no position was genotypable.
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...
Context in source publication
Context 1
... order to identify the SARS-CoV-2 strain, variant calling was performed on the sequenced amplicons. The set of identified variants in each sample was intersected with genomic coordinates associated with variants of circulating strains, to enable strain identification (Fig. 4). While four samples carried some not genotypable positions and whose strain genotyping may not be accurate, sample '128' was classified as 'Alpha' B.1.1.7 strain, samples '18', '80', '241' and '326' were classified as B.1.177 strain, sample '41' was classified as B.1.36.8 strain and sample '123' was classified as B.1.404 strain. All ...
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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.
