Evidence map›Paper›PMID 38850297›Full record

ArticleApplied microbiology and biotechnology2024

Quantifying variation across 16S rRNA gene sequencing runs in human microbiome studies.

Andrew J Hoisington, Christopher E Stamper, Joseph C Ellis, Christopher A Lowry, Lisa A Brenner

Abstract read
In one paragraph

Article in Applied microbiology and biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
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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

5 authors.

Andrew J HoisingtonVeterans Health Administration, Rocky Mountain Mental Illness Research Education and Clinical Center (MIRECC) for Veteran Suicide Prevention, Rocky Mountain Regional Veterans Affairs Medical Center (RMRVAMC), Aurora, CO, USA. andrew.hoisington@va.gov.
Christopher E StamperVeterans Health Administration, Rocky Mountain Mental Illness Research Education and Clinical Center (MIRECC) for Veteran Suicide Prevention, Rocky Mountain Regional Veterans Affairs Medical Center (RMRVAMC), Aurora, CO, USA.
Joseph C EllisNetellis LLC, Knoxville, TN, USA.
Christopher A LowryVeterans Health Administration, Rocky Mountain Mental Illness Research Education and Clinical Center (MIRECC) for Veteran Suicide Prevention, Rocky Mountain Regional Veterans Affairs Medical Center (RMRVAMC), Aurora, CO, USA.
Lisa A BrennerVeterans Health Administration, Rocky Mountain Mental Illness Research Education and Clinical Center (MIRECC) for Veteran Suicide Prevention, Rocky Mountain Regional Veterans Affairs Medical Center (RMRVAMC), Aurora, CO, USA.

Funding

Mindsource Brain Injury Network 140755U.S. Department of Veterans Affairs U.S. Department of Veterans Affairs
6 · The paper itself

Abstract

Recent microbiome research has incorporated a higher number of samples through more participants in a study, longitudinal studies, and metanalysis between studies. Physical limitations in a sequencing machine can result in samples spread across sequencing runs. Here we present the results of sequencing nearly 1000 16S rRNA gene sequences in fecal (stabilized and swab) and oral (swab) samples from multiple human microbiome studies and positive controls that were conducted with identical standard operating procedures. Sequencing was performed in the same center across 18 different runs. The simplified mock community showed limitations in accuracy, while precision (e.g., technical variation) was robust for the mock community and actual human positive control samples. Technical variation was the lowest for stabilized fecal samples, followed by fecal swab samples, and then oral swab samples. The order of technical variation stability was inverse of DNA concentrations (e.g., highest in stabilized fecal samples), highlighting the importance of DNA concentration in reproducibility and urging caution when analyzing low biomass samples. Coefficients of variation at the genus level also followed the same trend for lower variation with higher DNA concentrations. Technical variation across both sample types and the two human sampling locations was significantly less than the observed biological variation. Overall, this research providing comparisons between technical and biological variation, highlights the importance of using positive controls, and provides semi-quantified data to better understand variation introduced by sequencing runs. KEY POINTS: • Mock community and positive control accuracy were lower than precision. • Samples with lower DNA concentration had increased technical variation across sequencing runs. • Biological variation was significantly higher than technical variation due to sequencing runs.

Indexed as

DNA, BacterialFecesMicrobiotaRNA, Ribosomal, 16SSequence Analysis, DNABacteriaHigh-Throughput Nucleotide SequencingHumansMouthReproducibility of ResultsDNA, BacterialRNA, Ribosomal, 16SAccuracyMicrobiomePositive controlsPrecisionSequencing variance

Identifiers

PMID38850297
PMCPMC11162379

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.