Evidence map›Paper›PMID 23731183›Full record

ArticleJournal of proteome research2013

Use of theoretical peptide distributions in phosphoproteome analysis.

Mridul Kalita, Takhar Kasumov, Allan R Brasier, Rovshan G Sadygov

Abstract read
In one paragraph

Article in Journal of proteome research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mridul KalitaDepartment of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, Texas 77573, USA.
Takhar Kasumov
Allan R Brasier
Rovshan G Sadygov

Funding

UTMB Clinical and Translational Science AwardUL1TR000071 · NCATS · UNIVERSITY OF TEXAS MED BR GALVESTON · PI BRASIER, ALLAN R. · 2012 to 2014
$14.4M
NCATS NIH HHS UL1 TR000071NCATS NIH HHS UL1TR000071NHLBI NIH HHS HHSN268201000037CNIAID NIH HHS HHSN272200800048CPHS HHS NLBIHHSN268201000037C
6 · The paper itself

Abstract

The high mass accuracy and resolution of modern mass spectrometers provides new opportunities to employ theoretical peptide distributions in large-scale proteomic studies. We used theoretical distributions to study noise filtering and mass measurement errors and to examine mass-based differentiation of phosphorylated and nonphosphorylated peptides. Only the monoisotopic mass of the experimental precursor ion was necessary for this analysis. We found that peak deviations can be used to characterize the modification states of peptides in a sample. When applied to large-scale proteomic data sets, the peak deviation distribution can be used to filter chemical/electronic noise for singly charged species. Using peak deviation distributions, it is possible to separate the phosphorylated peptides from the nonphosphorylated peptides, enabling evaluation of the phosphoproteome content of a sample. Because this approach is simple, with light computational requirements, the analysis of theoretical peptide distributions has a significant potential for application to phosphoproteome analyses. For our studies we used publicly available data sets from three large-scale proteomic studies.

Indexed as

Models, TheoreticalAlgorithmsChromatography, LiquidMolecular WeightPeptidesPhosphorylationProteomeTandem Mass SpectrometryPeptidesProteome

Identifiers

PMID23731183
PMCPMC3758224

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.