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Comprehensive identification of sphingolipid species by in silico retention time and tandem mass spectral library

Overview of attention for article published in Journal of Cheminformatics, March 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

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1 blog
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9 X users
reddit
1 Redditor

Citations

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122 Mendeley
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Title
Comprehensive identification of sphingolipid species by in silico retention time and tandem mass spectral library
Published in
Journal of Cheminformatics, March 2017
DOI 10.1186/s13321-017-0205-3
Pubmed ID
Authors

Hiroshi Tsugawa, Kazutaka Ikeda, Wataru Tanaka, Yuya Senoo, Makoto Arita, Masanori Arita

Abstract

Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) is used for comprehensive metabolome and lipidome analyses. Compound identification relies on similarity matching of the retention time (RT), precursor m/z, isotopic ratio, and MS/MS spectrum with reference compounds. For sphingolipids, however, little information on the RT and MS/MS references is available. Negative-ion ESI-MS/MS is a useful method for the structural characterization of sphingolipids. We created theoretical MS/MS spectra for 21 sphingolipid classes in human and mouse (109,448 molecules), with substructure-level annotation of unique fragment ions by MS-FINDER software. The existence of ceramides with β-hydroxy fatty acids was confirmed in mouse tissues based on cheminformatic- and quantum chemical evidences. The RT of sphingo- and glycerolipid species was also predicted for our LC condition. With this information, MS-DIAL software for untargeted metabolome profiling could identify 415 unique structures including 282 glycerolipids and 133 sphingolipids from human cells (HEK and HeLa) and mouse tissues (ear and liver). MS-DIAL and MS-FINDER software programs can identify 42 lipid classes (21 sphingo- and 21 glycerolipids) with the in silico RT and MS/MS library. The library is freely available as Microsoft Excel files at the software section of our RIKEN PRIMe website (http://prime.psc.riken.jp/).

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 122 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
South Africa 1 <1%
Austria 1 <1%
Unknown 120 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 22%
Researcher 21 17%
Student > Master 14 11%
Student > Doctoral Student 9 7%
Professor 4 3%
Other 16 13%
Unknown 31 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 19%
Chemistry 18 15%
Agricultural and Biological Sciences 16 13%
Pharmacology, Toxicology and Pharmaceutical Science 7 6%
Computer Science 3 2%
Other 16 13%
Unknown 39 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 14 December 2022.
All research outputs
#3,051,999
of 24,995,611 outputs
Outputs from Journal of Cheminformatics
#286
of 938 outputs
Outputs of similar age
#53,822
of 313,426 outputs
Outputs of similar age from Journal of Cheminformatics
#10
of 22 outputs
Altmetric has tracked 24,995,611 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 938 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one has gotten more attention than average, scoring higher than 69% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 313,426 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.