Evidence map›Paper›PMID 25972773›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society

MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data.

Alexander Kaever, Manuel Landesfeind, Kirstin Feussner, Alina Mosblech, Ingo Heilmann, Burkhard Morgenstern, Ivo Feussner, Peter Meinicke

Abstract read
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

34 citing papers in PubMed.

  1. Article
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  3. Standardizing Methods for Shoot Metabolomics in Mycorrhizal Plants.Methods in molecular biology (Clifton, N.J.) · 2026
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  19. The glycosyltransferase UGT76E1 significantly contributes to 12-The Journal of biological chemistry · 2019
    Article
  20. Article
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.

Alexander KaeverDepartment of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany.
Manuel LandesfeindDepartment of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany.
Kirstin FeussnerDepartment of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, Georg-August-University Göttingen, Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany.
Alina MosblechDepartment of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, Georg-August-University Göttingen, Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany.
Ingo HeilmannDepartment of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, Georg-August-University Göttingen, Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany.
Burkhard MorgensternDepartment of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany.
Ivo FeussnerDepartment of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, Georg-August-University Göttingen, Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany.
Peter MeinickeDepartment of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A central aim in the evaluation of non-targeted metabolomics data is the detection of intensity patterns that differ between experimental conditions as well as the identification of the underlying metabolites and their association with metabolic pathways. In this context, the identification of metabolites based on non-targeted mass spectrometry data is a major bottleneck. In many applications, this identification needs to be guided by expert knowledge and interactive tools for exploratory data analysis can significantly support this process. Additionally, the integration of data from other omics platforms, such as DNA microarray-based transcriptomics, can provide valuable hints and thereby facilitate the identification of metabolites via the reconstruction of related metabolic pathways. We here introduce the MarVis-Pathway tool, which allows the user to identify metabolites by annotation of pathways from cross-omics data. The analysis is supported by an extensive framework for pathway enrichment and meta-analysis. The tool allows the mapping of data set features by ID, name, and accurate mass, and can incorporate information from adduct and isotope correction of mass spectrometry data. MarVis-Pathway was integrated in the MarVis-Suite (http://marvis.gobics.de), which features the seamless highly interactive filtering, combination, clustering, and visualization of omics data sets. The functionality of the new software tool is illustrated using combined mass spectrometry and DNA microarray data. This application confirms jasmonate biosynthesis as important metabolic pathway that is upregulated during the wound response of Arabidopsis plants.

Indexed as

Mass spectrometryMetabolic fingerprintingMetabolic pathwaysMetabolomicsSet enrichment analysisTranscriptomics

Identifiers

PMID25972773
PMCPMC4419191

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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.