Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11306-013-0539-4
Title: Precursor mass prediction by clustering ionization products in LC-MS-based metabolomics
Authors: Lee, T.S.
Ho, Y.S.
Yeo, H.C.
Lin, J.P.Y.
Lee, D.-Y. 
Keywords: Ionization products
LC-MS
Precursor mass prediction
Untargeted metabolomics
Issue Date: Dec-2013
Source: Lee, T.S., Ho, Y.S., Yeo, H.C., Lin, J.P.Y., Lee, D.-Y. (2013-12). Precursor mass prediction by clustering ionization products in LC-MS-based metabolomics. Metabolomics 9 (6) : 1301-1310. ScholarBank@NUS Repository. https://doi.org/10.1007/s11306-013-0539-4
Abstract: Liquid chromatography-mass spectrometry (LC-MS) is becoming the dominant technology in metabolomics, involving the comprehensive analysis of small molecules in biological systems. However, its use is still limited mainly by challenges in global high-throughput identification of metabolites: LC-MS data is highly complex, particularly due to the formation of multiple ionization products from individual metabolites. To address the limitation in metabolite identification, we developed a principled approach, designed to exploit the multi-dimensional information hidden in the data. The workflow first clusters candidate ionization products of the same metabolite together which typically have similar retention time, then searches for mass relationships among them in order to determine their ion types and metabolite identity. The robustness of our approach was demonstrated by its application to the LC-MS profiles of cell culture supernatant, which accurately predicted most of the known media components in the samples. Compared to conventional methods, our approach was able to generate significantly fewer candidate metabolites without missing out valid ones, thus reducing false-positive matches. Additionally, improved confidence in identification is achieved since each prediction comes with a probable combination of known ion types. Hence, our integrative workflow provides precursor mass predictions with high confidence by identifying various ionization products which account for a large proportion of detected peaks, thus minimizing false positives. © 2013 Springer Science+Business Media New York.
Source Title: Metabolomics
URI: http://scholarbank.nus.edu.sg/handle/10635/64446
ISSN: 15733882
DOI: 10.1007/s11306-013-0539-4
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

6
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

6
checked on Nov 22, 2017

Page view(s)

41
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.