Evidence map›Paper›PMID 37005774›Full record

ReviewMagnetic resonance in chemistry : MRC2023

Multiplatform untargeted metabolomics.

Micah J Jeppesen, Robert Powers

Abstract readReview
In one paragraph

Review in Magnetic resonance in chemistry : MRC, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing 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

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

  1. Pooled it
  2. Article
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  4. Review
  5. Article
  6. Beyond Genes: Metabolomic Evidence Indicates Potential Species-Level Differentiation in European Wild Rabbits.Journal of experimental zoology. Part A, Ecological and integrative physiology · 2026
    Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
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  16. SIMBA-GNN: mechanistic graph learning for microbiome prediction.NPJ systems biology and applications · 2025
    Article
  17. Article
  18. Review
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  20. Review
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.

Micah J JeppesenDepartment of Chemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588-0304, USA.
Robert PowersDepartment of Chemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588-0304, USA.ORCID 0000-0001-9948-6837

Funding

Targeted mass spectrometry approaches to understand CART processing and recepter interactionsP20GM113126 · NIGMS · UNIVERSITY OF NEBRASKA LINCOLN · PI GUO, JIANTAO · 2016 to 2025
$20.8M
The molecular mechanism linking respiratory NADH oxidation and virulence in Staphylococcus aureusR01AI148160 · NIAID · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI GENNIS, ROBERT B · 2020 to 2024
$2.6M
NIAID NIH HHS R01 AI148160NIGMS NIH HHS P20 GM113126NIH HHS R01 AI148160
6 · The paper itself

Abstract

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Indexed as

MetabolomeMetabolomicsHumansMultivariate AnalysisPrincipal Component AnalysisReproducibility of Resultsmass spectrometrymetabolite assignmentmetabolome coveragemetabolomicsmultiplatformnuclear magnetic resonance

Identifiers

PMID37005774
PMCPMC10948111

What OpenQuestion holds

Textmetadata
LicenceTDM
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.