Imperial College London
Alternate

Dr Elizabeth Want

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 3023e.want

 
 
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Location

 

E315CBurlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Graca:2022:10.1021/acs.analchem.1c03032,
author = {Graca, G and Cai, Y and Lau, C-H and Vorkas, PA and Lewis, MR and Want, EJ and Herrington, D and Ebbels, T},
doi = {10.1021/acs.analchem.1c03032},
journal = {Analytical Chemistry},
pages = {3446--3455},
title = {Automated annotation of untargeted all-ion fragmentation LC-MS metabolomics data with MetaboAnnotatoR},
url = {http://dx.doi.org/10.1021/acs.analchem.1c03032},
volume = {94},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Untargeted metabolomics and lipidomics LC-MS experiments produce complex datasets, usually containing tens of thousands of features from thousands of metabolites whose annotation requires additional MS/MS experiments and expert knowledge. All-ion fragmentation (AIF) LC-MS/MS acquisition provides fragmentation data at no additional experimental time cost. However, analysis of such datasets requires reconstruction of parent fragment relationships and annotation of the resulting pseudo-MS/MS spectra. Here we propose a novel approach for automated annotation of isotopologues, adducts and in-source fragments from AIF LC-MS datasets by combining correlation-based parent-fragment linking with molecular fragment matching. Our workflow focuses on a subset of features rather than trying to annotate the full dataset, saving time and simplifying the process. We demonstrate the workflow in three human serum datasets containing 599 features manually annotated by experts. Precision and recall values of 82- 92% and 82-85% respectively, were obtained for features found in the highest-rank scores (1-5). These results equal or outperform those obtained using MS-DIAL software, the current state-of-the-art for AIF data annotation. Further validation for other biological matrices and different instrument types showed variable precision (60-89%) and recall (10-88%) particularly for datasets dominated by non-lipid metabolites. The workflow is freely available as an open-source R package, MetaboAnnotatoR, together with the fragment libraries from Github (https://github.com/gggraca/MetaboAnnotatoR).
AU  - Graca,G
AU  - Cai,Y
AU  - Lau,C-H
AU  - Vorkas,PA
AU  - Lewis,MR
AU  - Want,EJ
AU  - Herrington,D
AU  - Ebbels,T
DO  - 10.1021/acs.analchem.1c03032
EP  - 3455
PY  - 2022///
SN  - 0003-2700
SP  - 3446
TI  - Automated annotation of untargeted all-ion fragmentation LC-MS metabolomics data with MetaboAnnotatoR
T2  - Analytical Chemistry
UR  - http://dx.doi.org/10.1021/acs.analchem.1c03032
UR  - https://pubs.acs.org/doi/10.1021/acs.analchem.1c03032
UR  - http://hdl.handle.net/10044/1/93070
VL  - 94
ER  -
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