Integration of DNA extraction, metabarcoding and an informatics pipeline to underpin a national citizen science honey monitoring scheme

Oliver, Anna E.; Newbold, Lindsay K. ORCID: https://orcid.org/0000-0001-8895-1406; Gweon, Hyun S.; Read, Daniel S. ORCID: https://orcid.org/0000-0001-8546-5154; Woodcock, Ben A. ORCID: https://orcid.org/0000-0003-0300-9951; Pywell, Richard F. ORCID: https://orcid.org/0000-0001-6431-9959. 2021 Integration of DNA extraction, metabarcoding and an informatics pipeline to underpin a national citizen science honey monitoring scheme. MethodsX, 8, 101303. 7, pp. https://doi.org/10.1016/j.mex.2021.101303

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Official URL: http://dx.doi.org/10.1016/j.mex.2021.101303

Abstract/Summary

Worldwide honeybees (Apis mellifera L.) are one of the most widely kept domesticated animals, supporting domestic and commercial livelihoods through the production of honey and wax, as well as in the delivery of pollination services to crops. Quantifying which plant species are foraged upon by honeybees provides insights into their nutritional status as well as patterns of landscape scale habitat utilization. Here we outline a rapid and reproducible methodology for identifying environmental DNA (eDNA) originating principally from pollen grains suspended within honey. The process is based on a DNA extraction incorporating vacuum filtration prior to universal eukaryotic internal transcribed spacer 2 region (ITS2) amplicon generation, sequencing and identification. To provide a pre-cursor to sequence phylotyping, we outline systems for error-corrected processing amplicon sequence variant abundance tables that removes chimeras. This methodology underpins the new UK National Honey Monitoring Scheme. • We compare the efficacy and speed of centrifugation and filtration systems for removing pollen from honey samples as a precursor to plant DNA barcoding. • We introduce the ‘HONEYPI’ informatics pipeline, an open access resource implemented in python 2.7, to ensure long-term reproducibility during the process of amplicon sequence variant classification.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.mex.2021.101303
UKCEH and CEH Sections/Science Areas:	Biodiversity (Science Area 2017-) Soils and Land Use (Science Area 2017-)
ISSN:	2215-0161
Additional Information. Not used in RCUK Gateway to Research.:	Open access paper - full text available via Official URL link.
Additional Keywords:	amplicon sequence variants (ASV), Illumina generated PhiX control library, ITS2, internal transcribed spacer 2, MiSeq platform, naive Bayesian classifier, vacuum filtration
NORA Subject Terms:	Biology and Microbiology
Date made live:	30 Mar 2021 09:57 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/529982

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.mex.2021.101303

UKCEH and CEH Sections/Science Areas:

Biodiversity (Science Area 2017-)
Soils and Land Use (Science Area 2017-)

ISSN:

2215-0161

Additional Information. Not used in RCUK Gateway to Research.:

Open access paper - full text available via Official URL link.

Additional Keywords:

amplicon sequence variants (ASV), Illumina generated PhiX control library, ITS2, internal transcribed spacer 2, MiSeq platform, naive Bayesian classifier, vacuum filtration

NORA Subject Terms:

Biology and Microbiology

Date made live:

30 Mar 2021 09:57 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/529982