An In Silico Database for Automated Feature Identification of High-Resolution Tandem Mass Spectrometry <sup>13</sup>C-Trimethylation Enhancement Using Diazomethane (<sup>13</sup>C-TrEnDi)-Modified Lipid Data-Reference-Cited by-同舟云学术

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An In Silico Database for Automated Feature Identification of High-Resolution Tandem Mass Spectrometry ¹³C-Trimethylation Enhancement Using Diazomethane (¹³C-TrEnDi)-Modified Lipid Data

Published:2023-11-06 Issue:12 Volume:34 Page:2722-2730
ISSN:1044-0305
Container-title:Journal of the American Society for Mass Spectrometry
language:en
Short-container-title:J. Am. Soc. Mass Spectrom.

Author:

Roberts Joshua A.¹,Rosales Christian A.¹,Wasslen Karl V.¹²,Radnoff Angela S.¹,Godbout Elena³,Diallo Jean-Simon³,Manthorpe Jeffrey M.¹²^ORCID,Smith Jeffrey C.¹²^ORCID

Affiliation:

1. Department of Chemistry, Carleton University, Ottawa, Ontario K1S 5B6, Canada

2. Carleton Mass Spectrometry Centre, Carleton University, Ottawa, Ontario K1S 5B6, Canada

3. Centre for Cancer Therapeutics, Ottawa Hospital Research Institute, 501 Smyth Road, Ottawa, Ontario K1H 8L6, Canada

Funder

Carleton University

Canada Foundation for Innovation

Ontario Research Foundation

Natural Sciences and Engineering Research Council of Canada

Government of Ontario

Publisher

American Chemical Society (ACS)

Subject

Spectroscopy,Structural Biology

Link

https://pubs.acs.org/doi/pdf/10.1021/jasms.3c00273

Reference23 articles.

1. Advances in Mass Spectrometry for Lipidomics

2. LipidBlast in silico tandem mass spectrometry database for lipid identification

3. LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data

4. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis

5. Lipid Annotator: Towards Accurate Annotation in Non-Targeted Liquid Chromatography High-Resolution Tandem Mass Spectrometry (LC-HRMS/MS) Lipidomics Using a Rapid and User-Friendly Software

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