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From qualitative to quantitative insect metabarcoding: an in tandem multilocus mosquito identification methodology

Kassela, K.; Kouvela, A.; De Courcy Williams, M.; Konstantinidis, K.; Rosa-Freitas, M. G.; Nearchou, A.; Gatzidou, E.; Veletza, S.; Boulougouris, G. C.; Dovrolis, N.; Karakasiliotis, I.

2020-11-22 genomics
10.1101/2020.11.22.393140 bioRxiv
Show 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,{chi} 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.

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