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Differentiating Positional Isomers of Nucleoside Modifications by Higher-Energy Collisional Dissociation Mass Spectrometry (HCD MS)

Overview of attention for article published in Journal of the American Society for Mass Spectrometry, June 2018
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

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2 X users
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Citations

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42 Mendeley
Title
Differentiating Positional Isomers of Nucleoside Modifications by Higher-Energy Collisional Dissociation Mass Spectrometry (HCD MS)
Published in
Journal of the American Society for Mass Spectrometry, June 2018
DOI 10.1007/s13361-018-1999-6
Pubmed ID
Authors

Manasses Jora, Andrew P. Burns, Robert L. Ross, Peter A. Lobue, Ruoxia Zhao, Cody M. Palumbo, Peter A. Beal, Balasubrahmanyam Addepalli, Patrick A. Limbach

Abstract

The analytical identification of positional isomers (e.g., 3-, N4-, 5-methylcytidine) within the > 160 different post-transcriptional modifications found in RNA can be challenging. Conventional liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) approaches rely on chromatographic separation for accurate identification because the collision-induced dissociation (CID) mass spectra of these isomers nearly exclusively yield identical nucleobase ions (BH2+) from the same molecular ion (MH+). Here, we have explored higher-energy collisional dissociation (HCD) as an alternative fragmentation technique to generate more informative product ions that can be used to differentiate positional isomers. LC-MS/MS of modified nucleosides characterized using HCD led to the creation of structure- and HCD energy-specific fragmentation patterns that generated unique fingerprints, which can be used to identify individual positional isomers even when they cannot be separated chromatographically. While particularly useful for identifying positional isomers, the fingerprinting capabilities enabled by HCD also offer the potential to generate HPLC-independent spectral libraries for the rapid analysis of modified ribonucleosides. Graphical Abstract ᅟ.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 29%
Student > Doctoral Student 4 10%
Student > Master 4 10%
Student > Bachelor 3 7%
Researcher 2 5%
Other 3 7%
Unknown 14 33%
Readers by discipline Count As %
Chemistry 9 21%
Biochemistry, Genetics and Molecular Biology 8 19%
Agricultural and Biological Sciences 5 12%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Chemical Engineering 1 2%
Other 3 7%
Unknown 15 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 11 March 2021.
All research outputs
#7,326,750
of 25,707,225 outputs
Outputs from Journal of the American Society for Mass Spectrometry
#986
of 3,875 outputs
Outputs of similar age
#117,475
of 342,529 outputs
Outputs of similar age from Journal of the American Society for Mass Spectrometry
#16
of 74 outputs
Altmetric has tracked 25,707,225 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 3,875 research outputs from this source. They receive a mean Attention Score of 3.8. This one has gotten more attention than average, scoring higher than 73% 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 342,529 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.