Evidence map›Paper›PMID 33824193›Full record

ArticlemSystems2021

BEExact: a Metataxonomic Database Tool for High-Resolution Inference of Bee-Associated Microbial Communities.

Brendan A Daisley, Gregor Reid

Abstract read
In one paragraph

Article in mSystems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
–field-weighted citation impact
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

35 citing papers in PubMed.

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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

2 authors.

Brendan A DaisleyDepartment of Microbiology & Immunology, The University of Western Ontario, London, Ontario, Canada.ORCID https://orcid.org/0000-0001-5999-5792
Gregor ReidDepartment of Microbiology & Immunology, The University of Western Ontario, London, Ontario, Canada gregor@uwo.ca.ORCID https://orcid.org/0000-0001-9658-5696

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput 16S rRNA gene sequencing technologies have robust potential to improve our understanding of bee (Hymenoptera: Apoidea)-associated microbial communities and their impact on hive health and disease. Despite recent computation algorithms now permitting exact inferencing of high-resolution exact amplicon sequence variants (ASVs), the taxonomic classification of these ASVs remains a challenge due to inadequate reference databases. To address this, we assemble a comprehensive data set of all publicly available bee-associated 16S rRNA gene sequences, systematically annotate poorly resolved identities via inclusion of 618 placeholder labels for uncultivated microbial dark matter, and correct for phylogenetic inconsistencies using a complementary set of distance-based and maximum likelihood correction strategies. To benchmark the resultant database (BEExact), we compare performance against all existing reference databases

Indexed as

16S rRNA gene sequencingbeesbioinformaticsendosymbiontsenvironmental microbiologyhost-microbe interactionsinvertebrate-microbe interactionsmetataxonomicsmicrobial communitiesmicrobial ecologymicrobial phylogeneticsmicrobiotapolymicrobial communitiestaxonomy

Identifiers

PMID33824193
PMCPMC8546966

What OpenQuestion holds

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LicenceCC BY
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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.