Evidence map›Paper›PMID 24816495›Full record

ReviewCurrent opinion in biotechnology2014

After the feature presentation: technologies bridging untargeted metabolomics and biology.

Kevin Cho, Nathaniel G Mahieu, Stephen L Johnson, Gary J Patti

Abstract readReview
In one paragraph

Review in Current opinion in biotechnology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Chemical Discovery in the Era of Metabolomics.Journal of the American Chemical Society · 2020
    Review
  12. Article
  13. Urinary Metabolites Diagnostic and Prognostic of Intrahepatic Cholangiocarcinoma.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2019
    Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Meta-mass shift chemical profiling of metabolomes from coral reefs.Proceedings of the National Academy of Sciences of the United States of America · 2017
    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

4 authors.

Kevin ChoDepartment of Genetics, Washington University School of Medicine, St. Louis, MO, United States; Department of Chemistry, Washington University, St. Louis, MO, United States; Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States.
Nathaniel G MahieuDepartment of Genetics, Washington University School of Medicine, St. Louis, MO, United States; Department of Chemistry, Washington University, St. Louis, MO, United States; Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States.
Stephen L JohnsonDepartment of Genetics, Washington University School of Medicine, St. Louis, MO, United States.
Gary J PattiDepartment of Genetics, Washington University School of Medicine, St. Louis, MO, United States; Department of Chemistry, Washington University, St. Louis, MO, United States; Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States. Electronic address: gjpattij@wustl.edu.

Funding

REGULATION OF ADULT STEM CELLS IN ZEBRAFISHR01GM056988 · NIGMS · WASHINGTON UNIVERSITY · PI JOHNSON, STEPHEN L · 1998 to 2016
$6.0M
DEVELOPING THE UNTARGETED METABOLOMIC WORKFLOW FOR HIGH-THROUGHPUT ANALYSESR01ES022181 · NIEHS · WASHINGTON UNIVERSITY · PI PATTI, GARY J · 2012 to 2016
$1.8M
NIA NIH HHS L30 AG038036NIEHS NIH HHS R01 ES022181NIGMS NIH HHS R01 GM056988
6 · The paper itself

Abstract

Liquid chromatography/mass spectrometry-based untargeted metabolomics is now an established experimental approach that is being broadly applied by many laboratories worldwide. Interpreting untargeted metabolomic data, however, remains a challenge and limits the translation of results into biologically relevant conclusions. Here we review emerging technologies that can be applied after untargeted profiling to extend biological interpretation of metabolomic data. These technologies include advances in bioinformatic software that enable identification of isotopes and adducts, comprehensive pathway mapping, deconvolution of MS(2) data, and tracking of isotopically labeled compounds. There are also opportunities to gain additional biological insight by complementing the metabolomic analysis of homogenized samples with recently developed technologies for metabolite imaging of intact tissues. To maximize the value of these emerging technologies, a unified workflow is discussed that builds on the traditional untargeted metabolomic pipeline. Particularly when integrated together, the combination of the advances highlighted in this review helps transform lists of masses and fold changes characteristic of untargeted profiling results into structures, absolute concentrations, pathway fluxes, and localization patterns that are typically needed to understand biology.

Indexed as

MetabolomeAnimalsChromatography, LiquidComputational BiologyIsotope LabelingMass SpectrometryMetabolomicsSoftware

Identifiers

PMID24816495
PMCPMC4111999

What OpenQuestion holds

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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.