Evidence map›Paper›PMID 32275430›Full record

ReviewJournal of the American Chemical Society2020

Chemical Discovery in the Era of Metabolomics.

Miriam Sindelar, Gary J Patti

Open access · greenAbstract readReview
In one paragraph

Review in Journal of the American Chemical Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
89citing papers in PubMed, 1 pooled it
9.9field-weighted citation impact, top 1% of its field
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

89 citing papers in PubMed, 1 synthesis or guideline pooled it, 178 citations in OpenAlex.

  1. Pooled it
  2. Leveraging 13C-Labeling to Assign Molecular Formulas to Unknown Yeast Metabolites.Journal of the American Society for Mass Spectrometry · 2026
    Article
  3. Article
  4. Review
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Metabolomics: Fundamentals, Methods, Analysis, Limits, and Recommendations.Advances in experimental medicine and biology · 2026
    Review
  13. Mass Spectrometry as a Tool for Metabolomics in Cancer.Advances in experimental medicine and biology · 2026
    Review
  14. Review
  15. Metabolomics Data Processing Using the Asari Toolkit.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Article

29 more citing papers are in PubMed but not listed here.

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 at 1 institution in 1 country.

Miriam SindelarDepartment of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, United States.
Gary J PattiDepartment of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, United States.ORCID 0000-0002-3748-6193
Washington University in St. Louis · US

Funding

Developing Metabolomic Technologies to Advance Environmental Exposure AnalysisR35ES028365 · NIEHS · WASHINGTON UNIVERSITY · PI PATTI, GARY J · 2017 to 2024
$5.9M
A COMPREHENSIVE RESOURCE FOR HIGH-THROUGHPUT PROFILING OF WORM AND ZEBRAFISH METABOLOMESR24OD024624 · OD · WASHINGTON UNIVERSITY · PI PATTI, GARY J · 2018 to 2021
$4.0M
A Comprehensive Platform for High-Throughput Profiling of the Human Reference MetabolomeU01CA235482 · NCI · WASHINGTON UNIVERSITY · PI PATTI, GARY J · 2018 to 2021
$1.8M
NCATS NIH HHS L30 TR000589NCI NIH HHS U01 CA235482NIEHS NIH HHS R35 ES028365NIH HHS R24 OD024624
6 · The paper itself

Abstract

Untargeted metabolomics aims to quantify the complete set of metabolites within a biological system, most commonly by liquid chromatography/mass spectrometry (LC/MS). Since nearly the inception of the field, compound identification has been widely recognized as the rate-limiting step of the experimental workflow. In spite of exponential increases in the size of metabolomic databases, which now contain experimental MS/MS spectra for over a half a million reference compounds, chemical structures still cannot be confidently assigned to many signals in a typical LC/MS dataset. The purpose of this Perspective is to consider why identification rates continue to be low in untargeted metabolomics. One rationalization is that many naturally occurring metabolites detected by LC/MS are true "novel" compounds that have yet to be incorporated into metabolomic databases. An alternative possibility, however, is that research data do not provide database matches because of informatic artifacts, chemical contaminants, and signal redundancies. Increasing evidence suggests that, for at least some sample types, many unidentifiable signals in untargeted metabolomics result from the latter rather than new compounds originating from the specimen being measured. The implications of these observations on chemical discovery in untargeted metabolomics are discussed.

Indexed as

MetabolomicsAnimalsChromatography, LiquidEscherichia coliHumansMass Spectrometry

Identifiers

PMID32275430
PMCPMC7310675
OpenAlexW3016036651

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.