An Automated Identification Tool for LC-MS Based Metabolomics Studies

Ke Shiuan Lynn; Chun Ju Chen; Chi En Tseng; Mei Ling Cheng; Wen Harn Pan

doi:10.1109/ICMLC48188.2019.8949193

An Automated Identification Tool for LC-MS Based Metabolomics Studies

Ke Shiuan Lynn
, Chun Ju Chen
, Chi En Tseng
, Mei Ling Cheng
, Wen Harn Pan

Department of Biomedical Sciences (Master's Program in Clinical Trials Assessment)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Liquid chromatography/mass spectrometer (LC/MS) has become one of the most popular analytical platform for metabolomics studies owing to its wide range of detectable polarity and molecular mass. However, metabolite identification remains quite costly and time-consuming in LC/MS-based metabolomics, mostly due to lower database integrity and a separated MS/MS spectra generation process. In this work, we constructed an automated, user-friendly, and freely available tool. From a peak list, the tool first groups peaks, which are usually associated with a metabolite, based on their retention time and abundance correlation across samples. In each group, different ions are annotated and the mass of the underlying metabolite is derived. Finally, the fragments are used to match with low-energy MS/MS spectra in public databases for metabolite identification. To identify metabolites without accessible MS/MS spectra, we have developed characteristic fragment and common substructure matches. Through the above approach, we anticipate facilitating the metabolite identification in LC-MS-based metabolomics studies.

Original language	English
Title of host publication	Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728128160
DOIs	https://doi.org/10.1109/ICMLC48188.2019.8949193
State	Published - 07 2019
Event	18th International Conference on Machine Learning and Cybernetics, ICMLC 2019 - Kobe, Japan Duration: 07 07 2019 → 10 07 2019

Publication series

Name	Proceedings - International Conference on Machine Learning and Cybernetics
Volume	2019-July
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	18th International Conference on Machine Learning and Cybernetics, ICMLC 2019
Country/Territory	Japan
City	Kobe
Period	07/07/19 → 10/07/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Automatic metabolite identification
Liquid chromatography mass spectrometer
Metabolomics

Access to Document

10.1109/ICMLC48188.2019.8949193

Cite this

Lynn, K. S., Chen, C. J., Tseng, C. E., Cheng, M. L., & Pan, W. H. (2019). An Automated Identification Tool for LC-MS Based Metabolomics Studies. In Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019 Article 8949193 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.1109/ICMLC48188.2019.8949193

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title = "An Automated Identification Tool for LC-MS Based Metabolomics Studies",

abstract = "Liquid chromatography/mass spectrometer (LC/MS) has become one of the most popular analytical platform for metabolomics studies owing to its wide range of detectable polarity and molecular mass. However, metabolite identification remains quite costly and time-consuming in LC/MS-based metabolomics, mostly due to lower database integrity and a separated MS/MS spectra generation process. In this work, we constructed an automated, user-friendly, and freely available tool. From a peak list, the tool first groups peaks, which are usually associated with a metabolite, based on their retention time and abundance correlation across samples. In each group, different ions are annotated and the mass of the underlying metabolite is derived. Finally, the fragments are used to match with low-energy MS/MS spectra in public databases for metabolite identification. To identify metabolites without accessible MS/MS spectra, we have developed characteristic fragment and common substructure matches. Through the above approach, we anticipate facilitating the metabolite identification in LC-MS-based metabolomics studies.",

keywords = "Automatic metabolite identification, Liquid chromatography mass spectrometer, Metabolomics",

author = "Lynn, \{Ke Shiuan\} and Chen, \{Chun Ju\} and Tseng, \{Chi En\} and Cheng, \{Mei Ling\} and Pan, \{Wen Harn\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 18th International Conference on Machine Learning and Cybernetics, ICMLC 2019 ; Conference date: 07-07-2019 Through 10-07-2019",

year = "2019",

month = jul,

doi = "10.1109/ICMLC48188.2019.8949193",

language = "英语",

series = "Proceedings - International Conference on Machine Learning and Cybernetics",

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Lynn, KS, Chen, CJ, Tseng, CE, Cheng, ML & Pan, WH 2019, An Automated Identification Tool for LC-MS Based Metabolomics Studies. in Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019., 8949193, Proceedings - International Conference on Machine Learning and Cybernetics, vol. 2019-July, IEEE Computer Society, 18th International Conference on Machine Learning and Cybernetics, ICMLC 2019, Kobe, Japan, 07/07/19. https://doi.org/10.1109/ICMLC48188.2019.8949193

An Automated Identification Tool for LC-MS Based Metabolomics Studies. / Lynn, Ke Shiuan; Chen, Chun Ju; Tseng, Chi En et al.
Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019. IEEE Computer Society, 2019. 8949193 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Automated Identification Tool for LC-MS Based Metabolomics Studies

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AU - Chen, Chun Ju

AU - Tseng, Chi En

AU - Cheng, Mei Ling

AU - Pan, Wen Harn

PY - 2019/7

Y1 - 2019/7

N2 - Liquid chromatography/mass spectrometer (LC/MS) has become one of the most popular analytical platform for metabolomics studies owing to its wide range of detectable polarity and molecular mass. However, metabolite identification remains quite costly and time-consuming in LC/MS-based metabolomics, mostly due to lower database integrity and a separated MS/MS spectra generation process. In this work, we constructed an automated, user-friendly, and freely available tool. From a peak list, the tool first groups peaks, which are usually associated with a metabolite, based on their retention time and abundance correlation across samples. In each group, different ions are annotated and the mass of the underlying metabolite is derived. Finally, the fragments are used to match with low-energy MS/MS spectra in public databases for metabolite identification. To identify metabolites without accessible MS/MS spectra, we have developed characteristic fragment and common substructure matches. Through the above approach, we anticipate facilitating the metabolite identification in LC-MS-based metabolomics studies.

AB - Liquid chromatography/mass spectrometer (LC/MS) has become one of the most popular analytical platform for metabolomics studies owing to its wide range of detectable polarity and molecular mass. However, metabolite identification remains quite costly and time-consuming in LC/MS-based metabolomics, mostly due to lower database integrity and a separated MS/MS spectra generation process. In this work, we constructed an automated, user-friendly, and freely available tool. From a peak list, the tool first groups peaks, which are usually associated with a metabolite, based on their retention time and abundance correlation across samples. In each group, different ions are annotated and the mass of the underlying metabolite is derived. Finally, the fragments are used to match with low-energy MS/MS spectra in public databases for metabolite identification. To identify metabolites without accessible MS/MS spectra, we have developed characteristic fragment and common substructure matches. Through the above approach, we anticipate facilitating the metabolite identification in LC-MS-based metabolomics studies.

KW - Automatic metabolite identification

KW - Liquid chromatography mass spectrometer

KW - Metabolomics

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ER -

An Automated Identification Tool for LC-MS Based Metabolomics Studies

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Keywords

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