Literature DB >> 34928621

Is Current Practice Adhering to Guidelines Proposed for Metabolite Identification in LC-MS Untargeted Metabolomics? A Meta-Analysis of the Literature.

Dritan Kodra1,2,3, Petros Pousinis1,2,3, Panagiotis A Vorkas4,5, Katerina Kademoglou1,2,3, Theodoros Liapikos1,2,3, Alexandros Pechlivanis1,2,3, Christina Virgiliou1,2,3, Ian D Wilson6, Helen Gika2,7,3, Georgios Theodoridis1,2,3.   

Abstract

Metabolite identification remains a bottleneck and a still unregulated area in untargeted LC-MS metabolomics. The metabolomics research community and, in particular, the metabolomics standards initiative (MSI) proposed minimum reporting standards for metabolomics including those for reporting metabolite identification as long ago as 2007. Initially, four levels were proposed ranging from level 1 (unambiguously identified analyte) to level 4 (unidentified analyte). This scheme was expanded in 2014, by independent research groups, to give five levels of confidence. Both schemes provided guidance to the researcher and described the logical steps that had to be made to reach a confident reporting level. These guidelines have been presented and discussed extensively, becoming well-known to authors, editors, and reviewers for academic publications. Despite continuous promotion within the metabolomics community, the application of such guidelines is questionable. The scope of this meta-analysis was to systematically review the current LC-MS-based literature and effectively determine the proportion of papers following the proposed guidelines. Also, within the scope of this meta-analysis was the measurement of the actual identification levels reported in the literature, that is to find how many of the published papers really reached full metabolite identification (level 1) and how many papers did not reach this level.

Entities:  

Keywords:  biomarker discovery; liquid chromatography; mass spectrometry; metabolic profiling; metabolite annotation; metabonomics; unknown metabolites

Mesh:

Year:  2021        PMID: 34928621     DOI: 10.1021/acs.jproteome.1c00841

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  2 in total

1.  MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition.

Authors:  Cheng S Yeung; Tim Beck; Joram M Posma
Journal:  Metabolites       Date:  2022-03-22

2.  Stability of Wheat Floret Metabolites during Untargeted Metabolomics Studies.

Authors:  Kristin Whitney; Gerardo Gracia-Gonzalez; Senay Simsek
Journal:  Metabolites       Date:  2022-01-11
  2 in total

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