Open Access
Journal of Cheminformatics, volume 2, issue 1, publication number 9
Organization of GC/MS and LC/MS metabolomics data into chemical libraries
1
Metabolon, Inc., Durham, USA
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Publication type: Journal Article
Publication date: 2010-10-18
Journal:
Journal of Cheminformatics
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 8.6
ISSN: 17582946, 17582946
Physical and Theoretical Chemistry
Computer Science Applications
Library and Information Sciences
Computer Graphics and Computer-Aided Design
Abstract
Metabolomics experiments involve generating and comparing small molecule (metabolite) profiles from complex mixture samples to identify those metabolites that are modulated in altered states (e.g., disease, drug treatment, toxin exposure). One non-targeted metabolomics approach attempts to identify and interrogate all small molecules in a sample using GC or LC separation followed by MS or MSn detection. Analysis of the resulting large, multifaceted data sets to rapidly and accurately identify the metabolites is a challenging task that relies on the availability of chemical libraries of metabolite spectral signatures. A method for analyzing spectrometry data to identify and Quantify Individual Components in a Sample, (QUICS), enables generation of chemical library entries from known standards and, importantly, from unknown metabolites present in experimental samples but without a corresponding library entry. This method accounts for all ions in a sample spectrum, performs library matches, and allows review of the data to quality check library entries. The QUICS method identifies ions related to any given metabolite by correlating ion data across the complete set of experimental samples, thus revealing subtle spectral trends that may not be evident when viewing individual samples and are likely to be indicative of the presence of one or more otherwise obscured metabolites. LC-MS/MS or GC-MS data from 33 liver samples were analyzed simultaneously which exploited the inherent biological diversity of the samples and the largely non-covariant chemical nature of the metabolites when viewed over multiple samples. Ions were partitioned by both retention time (RT) and covariance which grouped ions from a single common underlying metabolite. This approach benefitted from using mass, time and intensity data in aggregate over the entire sample set to reject outliers and noise thereby producing higher quality chemical identities. The aggregated data was matched to reference chemical libraries to aid in identifying the ion set as a known metabolite or as a new unknown biochemical to be added to the library. The QUICS methodology enabled rapid, in-depth evaluation of all possible metabolites (known and unknown) within a set of samples to identify the metabolites and, for those that did not have an entry in the reference library, to create a library entry to identify that metabolite in future studies.
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Citations by publishers
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120
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Springer Nature
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- We do not take into account publications without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Dehaven C. D. et al. Organization of GC/MS and LC/MS metabolomics data into chemical libraries // Journal of Cheminformatics. 2010. Vol. 2. No. 1. 9
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Dehaven C. D., Evans A. M., Dai H., Lawton K. A. Organization of GC/MS and LC/MS metabolomics data into chemical libraries // Journal of Cheminformatics. 2010. Vol. 2. No. 1. 9
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TY - JOUR
DO - 10.1186/1758-2946-2-9
UR - https://doi.org/10.1186/1758-2946-2-9
TI - Organization of GC/MS and LC/MS metabolomics data into chemical libraries
T2 - Journal of Cheminformatics
AU - Dehaven, Corey D
AU - Evans, Anne M.
AU - Dai, Hongping
AU - Lawton, Kay A
PY - 2010
DA - 2010/10/18 00:00:00
PB - Springer Nature
IS - 1
VL - 2
SN - 1758-2946
SN - 1758-2946
ER -
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@article{2010_Dehaven,
author = {Corey D Dehaven and Anne M. Evans and Hongping Dai and Kay A Lawton},
title = {Organization of GC/MS and LC/MS metabolomics data into chemical libraries},
journal = {Journal of Cheminformatics},
year = {2010},
volume = {2},
publisher = {Springer Nature},
month = {oct},
url = {https://doi.org/10.1186/1758-2946-2-9},
number = {1},
doi = {10.1186/1758-2946-2-9}
}