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Current controlled vocabularies are insufficient to uniquely map molecular entities to mass spectrometry signal

Overview of attention for article published in BMC Bioinformatics, April 2015
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Title
Current controlled vocabularies are insufficient to uniquely map molecular entities to mass spectrometry signal
Published in
BMC Bioinformatics, April 2015
DOI 10.1186/1471-2105-16-s7-s2
Pubmed ID
Authors

Rob Smith, Ryan M Taylor, John T Prince

Abstract

The comparison of analyte mass spectrometry precursor (MS1) signal is central to many proteomic (and other -omic) workflows. Standard vocabularies for mass spectrometry exist and provide good coverage for most experimental applications yet are insufficient for concise and unambiguous description of data concepts spanning the range of signal provenance from a molecular perspective (e.g. from charged peptides down to fine isotopes). Without a standard unambiguous nomenclature, literature searches, algorithm reproducibility and algorithm evaluation for MS-omics data processing are nearly impossible. We show how terms from current official ontologies are too vague or ambiguous to explicitly map molecular entities to MS signals and we illustrate the inconsistency and ambiguity of current colloquially used terms. We also propose a set of terms for MS1 signal that uniquely, succinctly and intuitively describe data concepts spanning the range of signal provenance from full molecule downs to fine isotopes. We suggest that additional community discussion of these terms should precede any further standardization efforts. We propose a novel nomenclature that spans the range of the required granularity to describe MS data processing from the perspective of the molecular provenance of the MS signal. The proposed nomenclature provides a chain of succinct and unique terms spanning the signal created by a charged molecule down through each of its constituent subsignals. We suggest that additional community discussion of these terms should precede any further standardization efforts.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 5%
Unknown 21 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 27%
Researcher 5 23%
Student > Master 2 9%
Lecturer 1 5%
Student > Doctoral Student 1 5%
Other 4 18%
Unknown 3 14%
Readers by discipline Count As %
Chemistry 5 23%
Computer Science 5 23%
Biochemistry, Genetics and Molecular Biology 3 14%
Agricultural and Biological Sciences 3 14%
Environmental Science 1 5%
Other 1 5%
Unknown 4 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 25 October 2019.
All research outputs
#14,827,133
of 22,830,751 outputs
Outputs from BMC Bioinformatics
#5,044
of 7,287 outputs
Outputs of similar age
#148,675
of 265,312 outputs
Outputs of similar age from BMC Bioinformatics
#101
of 139 outputs
Altmetric has tracked 22,830,751 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.