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Comprehensive comparison of in silico MS/MS fragmentation tools of the CASMI contest: database boosting is needed to achieve 93% accuracy

Overview of attention for article published in Journal of Cheminformatics, May 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 (83rd percentile)

Mentioned by

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21 tweeters

Citations

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74 Dimensions

Readers on

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126 Mendeley
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Title
Comprehensive comparison of in silico MS/MS fragmentation tools of the CASMI contest: database boosting is needed to achieve 93% accuracy
Published in
Journal of Cheminformatics, May 2017
DOI 10.1186/s13321-017-0219-x
Pubmed ID
Authors

Ivana Blaženović, Tobias Kind, Hrvoje Torbašinović, Slobodan Obrenović, Sajjan S. Mehta, Hiroshi Tsugawa, Tobias Wermuth, Nicolas Schauer, Martina Jahn, Rebekka Biedendieck, Dieter Jahn, Oliver Fiehn

Abstract

In mass spectrometry-based untargeted metabolomics, rarely more than 30% of the compounds are identified. Without the true identity of these molecules it is impossible to draw conclusions about the biological mechanisms, pathway relationships and provenance of compounds. The only way at present to address this discrepancy is to use in silico fragmentation software to identify unknown compounds by comparing and ranking theoretical MS/MS fragmentations from target structures to experimental tandem mass spectra (MS/MS). We compared the performance of four publicly available in silico fragmentation algorithms (MetFragCL, CFM-ID, MAGMa+ and MS-FINDER) that participated in the 2016 CASMI challenge. We found that optimizing the use of metadata, weighting factors and the manner of combining different tools eventually defined the ultimate outcomes of each method. We comprehensively analysed how outcomes of different tools could be combined and reached a final success rate of 93% for the training data, and 87% for the challenge data, using a combination of MAGMa+, CFM-ID and compound importance information along with MS/MS matching. Matching MS/MS spectra against the MS/MS libraries without using any in silico tool yielded 60% correct hits, showing that the use of in silico methods is still important.

Twitter Demographics

The data shown below were collected from the profiles of 21 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 126 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Brazil 1 <1%
Unknown 124 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 25%
Researcher 30 24%
Student > Master 9 7%
Student > Bachelor 7 6%
Student > Doctoral Student 6 5%
Other 19 15%
Unknown 24 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 20%
Chemistry 24 19%
Biochemistry, Genetics and Molecular Biology 17 13%
Computer Science 8 6%
Pharmacology, Toxicology and Pharmaceutical Science 7 6%
Other 17 13%
Unknown 28 22%

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 16 August 2020.
All research outputs
#2,558,862
of 22,497,510 outputs
Outputs from Journal of Cheminformatics
#254
of 824 outputs
Outputs of similar age
#46,237
of 289,970 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 1 outputs
Altmetric has tracked 22,497,510 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 824 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. 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 289,970 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 83% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them