Imperial College London
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ProfessorTimothyEbbels

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Professor of Biomedical Data Science
 
 
 
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Contact

 

+44 (0)20 7594 3160t.ebbels Website

 
 
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Location

 

315DBurlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Judge:2022:10.1007/s11306-022-01962-z,
author = {Judge, MT and Ebbels, TMD},
doi = {10.1007/s11306-022-01962-z},
journal = {Metabolomics},
title = {Problems, principles and progress in computational annotation of NMR metabolomics data},
url = {http://dx.doi.org/10.1007/s11306-022-01962-z},
volume = {18},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundCompound identification remains a critical bottleneck in the process of exploiting Nuclear Magnetic Resonance (NMR) metabolomics data, especially for 1H 1-dimensional (1H 1D) data. As databases of reference compound spectra have grown, workflows have evolved to rely heavily on their search functions to facilitate this process by generating lists of potential metabolites found in complex mixture data, facilitating annotation and identification. However, approaches for validating and communicating annotations are most often guided by expert knowledge, and therefore are highly variable despite repeated efforts to align practices and define community standards.Aim of reviewThis review is aimed at broadening the application of automated annotation tools by discussing the key ideas of spectral matching and beginning to describe a set of terms to classify this information, thus advancing standards for communicating annotation confidence. Additionally, we hope that this review will facilitate the growing collaboration between chemical data scientists, software developers and the NMR metabolomics community aiding development of long-term software solutions.Key scientific concepts of reviewWe begin with a brief discussion of the typical untargeted NMR identification workflow. We differentiate between annotation (hypothesis generation, filtering), and identification (hypothesis testing, verification), and note the utility of different NMR data features for annotation. We then touch on three parts of annotation: (1) generation of queries, (2) matching queries to reference data, and (3) scoring and confidence estimation of potential matches for verification. In doing so, we highlight existing approaches to automated and semi-automated annotation from the perspective of the structural information they utilize, as well as how this information can be represented computationally.
AU  - Judge,MT
AU  - Ebbels,TMD
DO  - 10.1007/s11306-022-01962-z
PY  - 2022///
SN  - 1573-3882
TI  - Problems, principles and progress in computational annotation of NMR metabolomics data
T2  - Metabolomics
UR  - http://dx.doi.org/10.1007/s11306-022-01962-z
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000894418700002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://link.springer.com/article/10.1007/s11306-022-01962-z
UR  - http://hdl.handle.net/10044/1/110377
VL  - 18
ER  -
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