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In silico analysis of accurate proteomics, complemented by selective isolation of peptides

Overview of attention for article published in Journal of Proteomics, May 2011
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

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Title
In silico analysis of accurate proteomics, complemented by selective isolation of peptides
Published in
Journal of Proteomics, May 2011
DOI 10.1016/j.jprot.2011.05.034
Pubmed ID
Authors

Yasset Perez-Riverol, Aniel Sánchez, Yassel Ramos, Alex Schmidt, Markus Müller, Lázaro Betancourt, Luis J. González, Roberto Vera, Gabriel Padron, Vladimir Besada

Abstract

Protein identification by mass spectrometry is mainly based on MS/MS spectra and the accuracy of molecular mass determination. However, the high complexity and dynamic ranges for any species of proteomic samples, surpass the separation capacity and detection power of the most advanced multidimensional liquid chromatographs and mass spectrometers. Only a tiny portion of signals is selected for MS/MS experiments and a still considerable number of them do not provide reliable peptide identification. In this article, an in silico analysis for a novel methodology of peptides and proteins identification is described. The approach is based on mass accuracy, isoelectric point (pI), retention time (t(R)) and N-terminal amino acid determination as protein identification criteria regardless of high quality MS/MS spectra. When the methodology was combined with the selective isolation methods, the number of unique peptides and identified proteins increases. Finally, to demonstrate the feasibility of the methodology, an OFFGEL-LC-MS/MS experiment was also implemented. We compared the more reliable peptide identified with MS/MS information, and peptide identified with three experimental features (pI, t(R), molecular mass). Also, two theoretical assumptions from MS/MS identification (selective isolation of peptides and N-terminal amino acid) were analyzed. Our results show that using the information provided by these features and selective isolation methods we could found the 93% of the high confidence protein identified by MS/MS with false-positive rate lower than 5%.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Turkey 1 2%
France 1 2%
Italy 1 2%
India 1 2%
Unknown 38 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 26%
Student > Ph. D. Student 6 14%
Student > Master 6 14%
Professor > Associate Professor 4 9%
Student > Bachelor 2 5%
Other 10 23%
Unknown 4 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 28%
Biochemistry, Genetics and Molecular Biology 10 23%
Environmental Science 3 7%
Chemistry 3 7%
Computer Science 3 7%
Other 7 16%
Unknown 5 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 07 November 2016.
All research outputs
#4,260,573
of 25,374,917 outputs
Outputs from Journal of Proteomics
#282
of 3,461 outputs
Outputs of similar age
#21,388
of 123,319 outputs
Outputs of similar age from Journal of Proteomics
#7
of 34 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,461 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 91% 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 123,319 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 34 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.