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An in silico MS/MS library for automatic annotation of novel FAHFA lipids

Overview of attention for article published in Journal of Cheminformatics, November 2015
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Title
An in silico MS/MS library for automatic annotation of novel FAHFA lipids
Published in
Journal of Cheminformatics, November 2015
DOI 10.1186/s13321-015-0104-4
Pubmed ID
Authors

Yan Ma, Tobias Kind, Arpana Vaniya, Ingrid Gennity, Johannes F. Fahrmann, Oliver Fiehn

Abstract

A new lipid class named 'fatty acid esters of hydroxyl fatty acids' (FAHFA) was recently discovered in mammalian adipose tissue and in blood plasma and some FAHFAs were found to be associated with type 2 diabetes. To facilitate the automatic annotation of FAHFAs in biological specimens, a tandem mass spectra (MS/MS) library is needed. Due to the limitation of the commercial available standard compounds, we proposed building an in silico MS/MS library to extend the coverage of molecules. We developed a computer-generated library with 3267 tandem mass spectra (MS/MS) for 1089 FAHFA species. FAHFA spectra were generated based on authentic standards with negative mode electrospray ionization and 10, 20, and 40 V collision induced dissociation at 4 spectra/s as used in in ultra-high performance liquid chromatography-QTOF mass spectrometry studies. However, positional information of the hydroxyl group is only obtained either at lower QTOF spectra acquisition rates of 1 spectrum/s or at the MS(3) level in ion trap instruments. Therefore, an additional set of 4290 fragment-rich MS/MS spectra was created to enable distinguishing positional FAHFA isomers. The library was generated based on ion fragmentations and ion intensities of FAHFA external reference standards, developing a heuristic model for fragmentation rules and extending these rules to large swaths of computer-generated structures of FAHFAs with varying chain lengths, degrees of unsaturation and hydroxyl group positions. Subsequently, we validated the new in silico library by discovering several new FAHFA species in egg yolk, showing that this library enables high-throughput screening of FAHFA lipids in various biological matrices. The developed library and templates are freely available for commercial or noncommercial use at http://fiehnlab.ucdavis.edu/staff/yanma/fahfa-lipid-library. This in silico MS/MS library allows users to annotate FAHFAs from accurate mass tandem mass spectra in an easy and fast manner with NIST MS Search or PepSearch software. The developing template is provided for advanced users to modify the parameters and export customized libraries according to their instrument features. Graphical abstractExample of experimental and in silico MS/MS spectra for FAHFA lipids.

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The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
United States 1 1%
Austria 1 1%
Brazil 1 1%
Unknown 68 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 25%
Researcher 15 21%
Student > Master 11 15%
Student > Doctoral Student 6 8%
Student > Bachelor 3 4%
Other 3 4%
Unknown 16 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 21%
Chemistry 12 17%
Biochemistry, Genetics and Molecular Biology 8 11%
Pharmacology, Toxicology and Pharmaceutical Science 5 7%
Medicine and Dentistry 3 4%
Other 8 11%
Unknown 21 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 November 2015.
All research outputs
#21,630,508
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#891
of 891 outputs
Outputs of similar age
#218,945
of 256,950 outputs
Outputs of similar age from Journal of Cheminformatics
#15
of 15 outputs
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