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- W3107817834 abstract "Abstract In the era of emergence and re-emergence of vector-borne diseases, a high throughput trap-based insect monitoring is essential for the identification of invasive species, study of mosquito populations and risk assessment of disease outbreaks. Insect DNA metabarcoding technology has emerged as a highly promising methodology for unbiased and large-scale surveillance. Despite significant attempts to introduce DNA metabarcoding in mosquito or other insect surveillance qualitative and quantitative metabarcoding remains a challenge. In the present study, we have developed a methodology of in-tandem identification and quantification using cytochrome oxidase subunit I (COI) combined with a secondary multilocus identification and quantification involving three loci of 28S ribosomal DNA. The presented methodology was able to identify individual species in pools of mosquitoes with 95.94% accuracy and resolve with high accuracy ( p = 1, χ 2 = 2.55) mosquito population composition providing a technology capable of revolutionizing mosquito surveillance through metabarcoding. The methodology, given the respective dataset, has the potential to be applied to various small animal populations." @default.
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- W3107817834 date "2020-11-22" @default.
- W3107817834 modified "2023-09-23" @default.
- W3107817834 title "From qualitative to quantitative insect metabarcoding: an in tandem multilocus mosquito identification methodology" @default.
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- W3107817834 doi "https://doi.org/10.1101/2020.11.22.393140" @default.
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