Next Article in Journal
Profiling Redox and Energy Coenzymes in Whole Blood, Tissue and Cells Using NMR Spectroscopy
Previous Article in Journal
Adenosine 5′-Triphosphate Metabolism in Red Blood Cells as a Potential Biomarker for Post-Exercise Hypotension and a Drug Target for Cardiovascular Protection
Article Menu
Issue 2 (June) cover image

Export Article

Open AccessReview
Metabolites 2018, 8(2), 31; https://doi.org/10.3390/metabo8020031

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

1
NIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA
2
State Key Laboratory of Food Science and Technology, School of Food Science of Jiangnan University, School of Food Science Synergetic Innovation Center of Food Safety and Nutrition, Wuxi 214122, China
3
Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
*
Author to whom correspondence should be addressed.
Received: 8 April 2018 / Revised: 26 April 2018 / Accepted: 6 May 2018 / Published: 10 May 2018
(This article belongs to the Section Thematic Reviews)
Full-Text   |   PDF [871 KB, uploaded 10 May 2018]   |  

Abstract

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included. View Full-Text
Keywords: tandem mass spectrometry; library search; in silico fragmentation; high resolution mass spectrometry; compound identification; metabolomics tandem mass spectrometry; library search; in silico fragmentation; high resolution mass spectrometry; compound identification; metabolomics
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Blaženović, I.; Kind, T.; Ji, J.; Fiehn, O. Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics. Metabolites 2018, 8, 31.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Metabolites EISSN 2218-1989 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top