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Curation of a reference database of COI sequences for insect identification through DNA metabarcoding: COins

DNA metabarcoding is a widespread approach for the molecular identification of organisms. While the associated wet-lab and data processing procedures are well established and highly efficient, the reference databases for taxonomic assignment can be implemented to improve the accuracy of identificati...

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Autores principales: Magoga, Giulia, Forni, Giobbe, Brunetti, Matteo, Meral, Aycan, Spada, Alberto, De Biase, Alessio, Montagna, Matteo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9261288/
https://www.ncbi.nlm.nih.gov/pubmed/35796594
http://dx.doi.org/10.1093/database/baac055
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author Magoga, Giulia
Forni, Giobbe
Brunetti, Matteo
Meral, Aycan
Spada, Alberto
De Biase, Alessio
Montagna, Matteo
author_facet Magoga, Giulia
Forni, Giobbe
Brunetti, Matteo
Meral, Aycan
Spada, Alberto
De Biase, Alessio
Montagna, Matteo
author_sort Magoga, Giulia
collection PubMed
description DNA metabarcoding is a widespread approach for the molecular identification of organisms. While the associated wet-lab and data processing procedures are well established and highly efficient, the reference databases for taxonomic assignment can be implemented to improve the accuracy of identifications. Insects are among the organisms for which DNA-based identification is most commonly used; yet, a DNA-metabarcoding reference database specifically curated for their species identification using software requiring local databases is lacking. Here, we present COins, a database of 5’ region cytochrome c oxidase subunit I sequences (COI-5P) of insects that includes over 532 000 representative sequences of >106 000 species specifically formatted for the QIIME2 software platform. Through a combination of automated and manually curated steps, we developed this database starting from all COI sequences available in the Barcode of Life Data System for insects, focusing on sequences that comply with several standards, including a species-level identification. COins was validated on previously published DNA-metabarcoding sequences data (bulk samples from Malaise traps) and its efficiency compared with other publicly available reference databases (not specific for insects). COins can allow an increase of up to 30% of species-level identifications and thus can represent a valuable resource for the taxonomic assignment of insects’ DNA-metabarcoding data, especially when species-level identification is needed https://doi.org/10.6084/m9.figshare.19130465.v1.
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spelling pubmed-92612882022-07-07 Curation of a reference database of COI sequences for insect identification through DNA metabarcoding: COins Magoga, Giulia Forni, Giobbe Brunetti, Matteo Meral, Aycan Spada, Alberto De Biase, Alessio Montagna, Matteo Database (Oxford) Original Article DNA metabarcoding is a widespread approach for the molecular identification of organisms. While the associated wet-lab and data processing procedures are well established and highly efficient, the reference databases for taxonomic assignment can be implemented to improve the accuracy of identifications. Insects are among the organisms for which DNA-based identification is most commonly used; yet, a DNA-metabarcoding reference database specifically curated for their species identification using software requiring local databases is lacking. Here, we present COins, a database of 5’ region cytochrome c oxidase subunit I sequences (COI-5P) of insects that includes over 532 000 representative sequences of >106 000 species specifically formatted for the QIIME2 software platform. Through a combination of automated and manually curated steps, we developed this database starting from all COI sequences available in the Barcode of Life Data System for insects, focusing on sequences that comply with several standards, including a species-level identification. COins was validated on previously published DNA-metabarcoding sequences data (bulk samples from Malaise traps) and its efficiency compared with other publicly available reference databases (not specific for insects). COins can allow an increase of up to 30% of species-level identifications and thus can represent a valuable resource for the taxonomic assignment of insects’ DNA-metabarcoding data, especially when species-level identification is needed https://doi.org/10.6084/m9.figshare.19130465.v1. Oxford University Press 2022-07-07 /pmc/articles/PMC9261288/ /pubmed/35796594 http://dx.doi.org/10.1093/database/baac055 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Article
Magoga, Giulia
Forni, Giobbe
Brunetti, Matteo
Meral, Aycan
Spada, Alberto
De Biase, Alessio
Montagna, Matteo
Curation of a reference database of COI sequences for insect identification through DNA metabarcoding: COins
title Curation of a reference database of COI sequences for insect identification through DNA metabarcoding: COins
title_full Curation of a reference database of COI sequences for insect identification through DNA metabarcoding: COins
title_fullStr Curation of a reference database of COI sequences for insect identification through DNA metabarcoding: COins
title_full_unstemmed Curation of a reference database of COI sequences for insect identification through DNA metabarcoding: COins
title_short Curation of a reference database of COI sequences for insect identification through DNA metabarcoding: COins
title_sort curation of a reference database of coi sequences for insect identification through dna metabarcoding: coins
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9261288/
https://www.ncbi.nlm.nih.gov/pubmed/35796594
http://dx.doi.org/10.1093/database/baac055
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