Evidence map›Paper›PMID 24725457›Full record

ReviewClinical proteomics2014

Glycoproteomic and glycomic databases.

Deniz Baycin Hizal, Daniel Wolozny, Joseph Colao, Elena Jacobson, Yuan Tian, Sharon S Krag, Michael J Betenbaugh, Hui Zhang

Abstract readReview
In one paragraph

Review in Clinical proteomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. FGDB: Database of follicle stimulating hormone glycans.Computational and structural biotechnology journal · 2021
    Article
  5. Review
  6. Review
  7. Review
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
  13. Review
  14. Review
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

8 authors.

Deniz Baycin HizalDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Daniel WoloznyDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Joseph ColaoDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Elena JacobsonDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Yuan TianDepartment of Pathology, Johns Hopkins University, Baltimore, MD, USA.
Sharon S KragDepartment of Biochemistry and Molecular Biology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Michael J BetenbaughDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Hui ZhangDepartment of Pathology, Johns Hopkins University, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein glycosylation serves critical roles in the cellular and biological processes of many organisms. Aberrant glycosylation has been associated with many illnesses such as hereditary and chronic diseases like cancer, cardiovascular diseases, neurological disorders, and immunological disorders. Emerging mass spectrometry (MS) technologies that enable the high-throughput identification of glycoproteins and glycans have accelerated the analysis and made possible the creation of dynamic and expanding databases. Although glycosylation-related databases have been established by many laboratories and institutions, they are not yet widely known in the community. Our study reviews 15 different publicly available databases and identifies their key elements so that users can identify the most applicable platform for their analytical needs. These databases include biological information on the experimentally identified glycans and glycopeptides from various cells and organisms such as human, rat, mouse, fly and zebrafish. The features of these databases - 7 for glycoproteomic data, 6 for glycomic data, and 2 for glycan binding proteins are summarized including the enrichment techniques that are used for glycoproteome and glycan identification. Furthermore databases such as Unipep, GlycoFly, GlycoFish recently established by our group are introduced. The unique features of each database, such as the analytical methods used and bioinformatical tools available are summarized. This information will be a valuable resource for the glycobiology community as it presents the analytical methods and glycosylation related databases together in one compendium. It will also represent a step towards the desired long term goal of integrating the different databases of glycosylation in order to characterize and categorize glycoproteins and glycans better for biomedical research.

Identifiers

PMID24725457
PMCPMC3996109

What OpenQuestion holds

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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.