Evidence map›Paper›PMID 34551203›Full record

ReviewMolecular ecology resources2022

Strategies for sample labelling and library preparation in DNA metabarcoding studies.

Kristine Bohmann, Vasco Elbrecht, Christian Carøe, Iliana Bista, Florian Leese, Michael Bunce, Douglas W Yu, Mathew Seymour, Alex J Dumbrell, Simon Creer

Open access · hybridAbstract readReview
In one paragraph

Review in Molecular ecology resources, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 1 pooled it
18.8field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

49 citing papers in PubMed, 1 synthesis or guideline pooled it, 141 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 9 institutions in 7 countries.

Kristine BohmannFaculty of Health and Medical Sciences, Section for Evolutionary Genomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark.ORCID https://orcid.org/0000-0001-7907-064X
Vasco ElbrechtDepartment of Environmental Systems Science, ETH Zurich, Zürich, Switzerland.ORCID https://orcid.org/0000-0003-4672-7099
Christian CarøeFaculty of Health and Medical Sciences, Section for Evolutionary Genomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark.ORCID https://orcid.org/0000-0001-9601-6768
Iliana BistaDepartment of Genetics, University of Cambridge, Cambridge, UK.
Florian LeeseAquatic Ecosystem Research, Faculty of Biology, University of Duisburg-Essen, Essen, Germany.ORCID https://orcid.org/0000-0002-5465-913X
Michael BunceTrace and Environmental DNA (TrEnD) Laboratory, School of Molecular and Life Sciences, Curtin University, Perth, WA, Australia.ORCID https://orcid.org/0000-0002-0302-4206
Douglas W YuState Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.ORCID https://orcid.org/0000-0001-8551-5609
Mathew SeymourDepartment of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden.ORCID https://orcid.org/0000-0002-3654-4857
Alex J DumbrellSchool of Life Sciences, University of Essex, Colchester, UK.ORCID https://orcid.org/0000-0001-6282-3043
Simon CreerMolecular Ecology and Evolution Group, School of Natural Sciences, Bangor University, Gwynedd, UK.ORCID https://orcid.org/0000-0003-3124-3550
University of Copenhagen · DKBangor University · GBCurtin University · AUETH Zurich · CHSwedish University of Agricultural Sciences · SEUniversity of Cambridge · GBUniversity of Duisburg-Essen · DEUniversity of East Anglia · GBUniversity of Essex · GB

Funding

Cancer Research UK 27826Wellcome Trust
6 · The paper itself

Abstract

Metabarcoding of DNA extracted from environmental or bulk specimen samples is increasingly used to profile biota in basic and applied biodiversity research because of its targeted nature that allows sequencing of genetic markers from many samples in parallel. To achieve this, PCR amplification is carried out with primers designed to target a taxonomically informative marker within a taxonomic group, and sample-specific nucleotide identifiers are added to the amplicons prior to sequencing. The latter enables assignment of the sequences back to the samples they originated from. Nucleotide identifiers can be added during the metabarcoding PCR and during "library preparation", that is, when amplicons are prepared for sequencing. Different strategies to achieve this labelling exist. All have advantages, challenges and limitations, some of which can lead to misleading results, and in the worst case compromise the fidelity of the metabarcoding data. Given the range of questions addressed using metabarcoding, ensuring that data generation is robust and fit for the chosen purpose is critically important for practitioners seeking to employ metabarcoding for biodiversity assessments. Here, we present an overview of the three main workflows for sample-specific labelling and library preparation in metabarcoding studies on Illumina sequencing platforms; one-step PCR, two-step PCR, and tagged PCR. Further, we distill the key considerations for researchers seeking to select an appropriate metabarcoding strategy for their specific study. Ultimately, by gaining insights into the consequences of different metabarcoding workflows, we hope to further consolidate the power of metabarcoding as a tool to assess biodiversity across a range of applications.

Indexed as

BiodiversityDNA Barcoding, TaxonomicDNA PrimersGene LibraryPolymerase Chain ReactionDNA Primersamplicon sequencingbiodiversity assessmenteDNAenvironmental DNAhigh-throughput sequencingIllumina sequencinglibrary preparation

Identifiers

PMID34551203
PMCPMC9293284
OpenAlexW3160349507

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.