Evidence map›Paper›PMID 39659589›Full record

ArticleBioinformatics advances2024

MeTEor: an R Shiny app for exploring longitudinal metabolomics data.

Gordon Grabert, Daniel Dehncke, Tushar More, Markus List, Anke R M Kraft, Markus Cornberg, Karsten Hiller, Tim Kacprowski

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Gordon GrabertDivision Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Lower Saxony 38106, Germany.ORCID https://orcid.org/0000-0001-5499-9970
Daniel DehnckeDivision Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Lower Saxony 38106, Germany.ORCID https://orcid.org/0009-0004-5816-2886
Tushar MoreBraunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Lower Saxony 38106, Germany.ORCID https://orcid.org/0000-0001-7094-1760
Markus ListData Science in Systems Biology, School of Life Sciences, Technical University of Munich, Munich, Bavaria 85354, Germany.ORCID https://orcid.org/0000-0002-0941-4168
Anke R M KraftCentre for Individualised Infection Medicine (CiiM), Joint Venture Between Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Lower Saxony 30625, Germany.ORCID https://orcid.org/0000-0002-9666-0753
Markus CornbergCentre for Individualised Infection Medicine (CiiM), Joint Venture Between Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Lower Saxony 30625, Germany.ORCID https://orcid.org/0000-0002-9141-8001
Karsten HillerBraunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Lower Saxony 38106, Germany.ORCID https://orcid.org/0000-0001-9322-5820
Tim KacprowskiDivision Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Lower Saxony 38106, Germany.ORCID https://orcid.org/0000-0002-5393-2413

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: The availability of longitudinal omics data is increasing in metabolomics research. Viewing metabolomics data over time provides detailed insight into biological processes and fosters understanding of how systems react over time. However, the analysis of longitudinal metabolomics data poses various challenges, both in terms of statistical evaluation and visualization. Results: To make explorative analysis of longitudinal data readily available to researchers without formal background in computer science and programming, we present MEtabolite Trajectory ExplORer (MeTEor). MeTEor is an R Shiny app providing a comprehensive set of statistical analysis methods. To demonstrate the capabilities of MeTEor, we replicated the analysis of metabolomics data from a previously published study on COVID-19 patients. Availability and implementation: MeTEor is available as an R package and as a Docker image. Source code and instructions for setting up the app can be found on GitHub (https://github.com/scibiome/meteor). The Docker image is available at Docker Hub (https://hub.docker.com/r/gordomics/meteor). MeTEor has been tested on Microsoft Windows, Unix/Linux, and macOS.

Identifiers

PMID39659589
PMCPMC11631383

What OpenQuestion holds

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LicenceCC BY
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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.