Evidence map›Paper›PMID 33986735›Full record

ArticleFrontiers in microbiology2021

Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment.

Megan S Beaudry, Jincheng Wang, Troy J Kieran, Jesse Thomas, Natalia J Bayona-Vásquez, Bei Gao, Alison Devault, Brian Brunelle, Kun Lu, Jia-Sheng Wang and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
2.4field-weighted citation impact, top 10% 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

22 citing papers in PubMed, 44 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

12 authors at 3 institutions in 1 country.

Megan S BeaudryDepartment of Environmental Health Science, University of Georgia, Athens, GA, United States.
Jincheng WangDepartment of Environmental Health Science, University of Georgia, Athens, GA, United States.
Troy J KieranDepartment of Environmental Health Science, University of Georgia, Athens, GA, United States.
Jesse ThomasDepartment of Environmental Health Science, University of Georgia, Athens, GA, United States.
Natalia J Bayona-VásquezDepartment of Environmental Health Science, University of Georgia, Athens, GA, United States.
Bei GaoDepartment of Environmental Health Science, University of Georgia, Athens, GA, United States.
Alison DevaultDaicel Arbor Biosciences, Ann Arbor, MI, United States.
Brian BrunelleDaicel Arbor Biosciences, Ann Arbor, MI, United States.
Kun LuDepartment of Environmental Health Science, University of Georgia, Athens, GA, United States.
Jia-Sheng WangDepartment of Environmental Health Science, University of Georgia, Athens, GA, United States.
Olin E RhodesSavannah River Ecology Laboratory, University of Georgia, Aiken, SC, United States.
Travis C GlennDepartment of Environmental Health Science, University of Georgia, Athens, GA, United States.
University of Georgia · USAastrom Biosciences (United States) · USSavannah River National Laboratory · US

Funding

UNC-CH CENTER FOR ENVIRONMENTAL HEALTH &SUSCEPTIBILITYP30ES010126 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Stephanie Engel · 2001 to 2026
$36.3M
The UNC Chapel Hill Superfund Research Program (UNC-SRP)P42ES031007 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Rebecca Fry · 2020 to 2026
$22.2M
Functional interaction between the gut microbiome and arsenic exposureR01ES024950 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LU, KUN · 2015 to 2019
$2.5M
NIEHS NIH HHS P30 ES010126NIEHS NIH HHS P42 ES031007NIEHS NIH HHS R01 ES024950
6 · The paper itself

Abstract

Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in genomic scope. In contrast, shotgun metagenomic methods sample more genomic regions with fewer sequence acquisition biases, but are much more expensive (even with moderate sequencing depth) and computationally challenging. Here, we develop a set of 16S rRNA sequence capture baits that offer a potential middle ground with the advantages from both approaches for investigating microbial communities. These baits cover the diversity of all 16S rRNA sequences available in the Greengenes (v. 13.5) database, with no sequence having <78% sequence identity to at least one bait for all segments of 16S. The use of our baits provide comparable results to 16S amplicon libraries and shotgun metagenomic libraries when assigning taxonomic units from 16S sequences within the metagenomic reads. We demonstrate that 16S rRNA capture baits can be used on a range of microbial samples (i.e., mock communities and rodent fecal samples) to increase the proportion of 16S rRNA sequences (average > 400-fold) and decrease analysis time to obtain consistent community assessments. Furthermore, our study reveals that bioinformatic methods used to analyze sequencing data may have a greater influence on estimates of community composition than library preparation method used, likely due in part to the extent and curation of the reference databases considered. Thus, enriching existing aliquots of shotgun metagenomic libraries and obtaining modest numbers of reads from them offers an efficient orthogonal method for assessment of bacterial community composition.

Indexed as

ampliconmicrobial diversitymicrobiomemock communitiesnext generation sequencingshotgun librariestarget enrichment

Identifiers

PMID33986735
PMCPMC8110821
OpenAlexW3157172305

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.