Evidence map›Paper›PMID 29748461›Full record

ReviewMetabolites2018

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

Ivana Blaženović, Tobias Kind, Jian Ji, Oliver Fiehn

Abstract readReview
In one paragraph

Review in Metabolites, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 344 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
344citing papers in PubMed, 1 pooled it
–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

344 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Virulence · 2026
    Article
  4. Virulence · 2026
    Article
  5. Article
  6. Metabolomics Based Biomarkers of Green Beans Intake.Molecular nutrition & food research · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article

284 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

4 authors.

Ivana BlaženovićNIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA. iblazenovic@ucdavis.edu.
Tobias KindNIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA. tkind@ucdavis.edu.
Jian JiState Key Laboratory of Food Science and Technology, School of Food Science of Jiangnan University, School of Food Science Synergetic Innovation Center of Food Safety and Nutrition, Wuxi 214122, China. jijianjndx@126.com.
Oliver FiehnNIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA. ofiehn@ucdavis.edu.ORCID 0000-0002-6261-8928

Funding

West Coast Central Comprehensive Metabolomics Resource Core (WC3MRC)U24DK097154 · NIDDK · UNIVERSITY OF CALIFORNIA AT DAVIS · PI FIEHN, OLIVER · 2012 to 2017
$9.9M
Genomewide Association Study of Lipid Response to Fenofibrate and Dietary FatR01HL091357 · NHLBI · UNIVERSITY OF KENTUCKY · PI ARNETT, DONNA K · 2008 to 2018
$5.8M
NHLBI NIH HHS R01 HL091357NIDDK NIH HHS U24 DK097154
6 · The paper itself

Abstract

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.

Indexed as

compound identificationhigh resolution mass spectrometryin silico fragmentationlibrary searchmetabolomicstandem mass spectrometry

Identifiers

PMID29748461
PMCPMC6027441

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.