Evidence map›Paper›PMID 37110122›Full record

ArticleMetabolites2023

Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools.

Elizabeth J Parker, Kathryn C Billane, Nichola Austen, Anne Cotton, Rachel M George, David Hopkins, Janice A Lake, James K Pitman, James N Prout, Heather J Walker and 2 more

Abstract read
In one paragraph

Article in Metabolites, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. KMD-Enhanced MALDI-ToF MS for Polymer Biodegradation Analysis.Journal of the American Society for Mass Spectrometry · 2026
    Article
  3. Article
  4. Article
  5. Targeted Metabolomic Methods forJournal of proteome research · 2025
    Article
  6. Article
  7. 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

12 authors.

Elizabeth J ParkerSchool of Biosciences, University of Sheffield, Sheffield S10 2TN, UK.
Kathryn C BillaneSchool of Biosciences, University of Sheffield, Sheffield S10 2TN, UK.
Nichola AustenDepartment of Biology, University of Oxford, Oxford OX1 3RB, UK.ORCID 0000-0002-4873-5861
Anne CottonSchool of Biosciences, University of Sheffield, Sheffield S10 2TN, UK.
Rachel M GeorgebiOMICS Mass Spectrometry Centre, University of Sheffield, Sheffield S10 2TN, UK.
David HopkinsDepartment of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK.
Janice A LakeDepartment of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK.
James K PitmanSchool of Biosciences, University of Sheffield, Sheffield S10 2TN, UK.
James N ProutSchool of Biosciences, University of Sheffield, Sheffield S10 2TN, UK.
Heather J WalkerbiOMICS Mass Spectrometry Centre, University of Sheffield, Sheffield S10 2TN, UK.ORCID 0000-0002-6815-0465
Alex WilliamsSchool of Biosciences, University of Sheffield, Sheffield S10 2TN, UK.
Duncan D CameronDepartment of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK.ORCID 0000-0002-5439-6544

Funding

Biotechnology and Biological Sciences Research Council BB/M011151/1Biotechnology and Biological Sciences Research Council BB/T010789/1
6 · The paper itself

Abstract

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the 'correct' pipeline isn't straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.

Indexed as

bioinformaticsmass-spectrometrymetabolomicsopen-sourceuntargeted

Identifiers

PMID37110122
PMCPMC10142740

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.