ArticlePloS one2022
Next generation biosecurity: Towards genome based identification to prevent spread of agronomic pests and pathogens using nanopore sequencing.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 12 citations in OpenAlex.
- Metabarcoding with Illumina and Oxford Nanopore Technologies provides complementary insights into tree seed mycobiota.Environmental microbiome · 2025Article
- Genomics in plant pathogen identification and control.Frontiers in plant science · 2025Review
- Chromosome Structural Rearrangements in Invasive Haplodiploid Ambrosia Beetles Revealed by the Genomes of Euwallacea fornicatus (Eichhoff) and Euwallacea similis (Ferrari) (Coleoptera, Curculionidae, Scolytinae).Genome biology and evolution · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The unintentional movement of agronomic pests and pathogens is steadily increasing due to the intensification of global trade. Being able to identify accurately and rapidly early stages of an invasion is critical for developing successful eradication or management strategies. For most invasive organisms, molecular diagnostics is today the method of choice for species identification. However, the currently implemented tools are often developed for certain taxa and need to be adapted for new species, making them ill-suited to cope with the current constant increase in new invasive species. To alleviate this impediment, we developed a fast and accurate sequencing tool allowing to modularly obtain genetic information at different taxonomical levels. Using whole genome amplification (WGA) followed by Oxford nanopore MinION sequencing, our workflow does not require any a priori knowledge on the investigated species and its classification. While mainly focusing on harmful plant pathogenic insects, we also demonstrate the suitability of our workflow for the molecular identification of bacteria (Erwinia amylovora and Escherichia coli), fungi (Cladosporium herbarum, Colletotrichum salicis, Neofabraea alba) and nematodes (Globodera rostochiensis). On average, the pairwise identity between the generated consensus sequences and best GenBank BLAST matches was 99.6 ± 0.6%. Additionally, assessing the generated insect genomic dataset, the potential power of the workflow to detect pesticide resistance genes, as well as arthropod-infecting viruses and endosymbiotic bacteria is demonstrated.
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