Evidence map›Paper›PMID 38421266›Full record

ArticleMicrobial genomics2024

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling.

Monica Steffi Matchado, Malte Rühlemann, Sandra Reitmeier, Tim Kacprowski, Fabian Frost, Dirk Haller, Jan Baumbach, Markus List

Abstract read
In one paragraph

Article in Microbial genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
120citing papers in PubMed, 1 pooled it
–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

120 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
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  11. TwoInternational journal of molecular sciences · 2026
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  14. The Effects of Vitamin DAnimals : an open access journal from MDPI · 2026
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  19. Associations of Breed andMicroorganisms · 2026
    Article
  20. Roux-en-Y gastric bypass alters microbial circadian rhythms with links to metabolic improvement.American journal of physiology. Gastrointestinal and liver physiology · 2026
    Article

60 more citing papers are in PubMed but not listed here.

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

8 authors.

Monica Steffi MatchadoData Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
Malte RühlemannInstitute of Clinical Molecular Biology, Kiel University, Kiel, Germany.
Sandra ReitmeierZIEL - Institute for Food & Health, Core Facility Microbiome, Technical University of Munich, Freising, Germany.
Tim KacprowskiDivision Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.
Fabian FrostDepartment of Medicine A, University Medicine Greifswald, Greifswald, Germany.
Dirk HallerZIEL - Institute for Food & Health, Core Facility Microbiome, Technical University of Munich, Freising, Germany.
Jan BaumbachInstitute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
Markus ListData Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

Indexed as

MetagenomeMicrobiotaAlgorithmsGenes, rRNAHumansRNA, Ribosomal, 16SRNA, Ribosomal, 16S16S copy number normalization16S rRNA gene sequencingmetagenome shotgun sequencingmicrobial functionsPanFPPICRUSt2

Identifiers

PMID38421266
PMCPMC10926695

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.