Evidence map›Paper›PMID 26728195›Full record

ReviewMass spectrometry reviews2017

Algorithms and design strategies towards automated glycoproteomics analysis.

Han Hu, Kshitij Khatri, Joseph Zaia

Abstract readReview
In one paragraph

Review in Mass spectrometry reviews, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing 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

46 citing papers in PubMed.

  1. Article
  2. Article
  3. Can We BoostJournal of the American Society for Mass Spectrometry · 2024
    Article
  4. Review
  5. Systems-Wide Site-Specific Analysis of Glycoproteins.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. The Hitchhiker's guide to glycoproteomics.Biochemical Society transactions · 2021
    Review
  12. Review
  13. Article
  14. Calculating Glycoprotein Similarities From Mass Spectrometric Data.Molecular & cellular proteomics : MCP · 2021
    Review
  15. The Role of Data-Independent Acquisition for Glycoproteomics.Molecular & cellular proteomics : MCP · 2021
    Review
  16. Review
  17. Review
  18. Article
  19. Bioinformatics in Immunoglobulin Glycosylation Analysis.Experientia supplementum (2012) · 2021
    Article
  20. 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

3 authors.

Han HuBioinformatics Program, Boston University, Boston, MA 02215.
Kshitij KhatriCenter for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, MA 02118.
Joseph ZaiaCenter for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, MA 02118.

Funding

XI INTERNATIONAL SYMPOSIUM ON AMYLOIDOSISP41RR010888 · NCRR · BOSTON UNIVERSITY MEDICAL CAMPUS · PI ZAIA, JOSEPH · 1996 to 2011
$18.6M
Mass Spectrometry Resource for Biology and MedicineP41GM104603 · NIGMS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI COSTELLO, CATHERINE E. · 2012 to 2017
$10.4M
Post-biosynthetic Remodeling of Heparan SulfateR01HL098950 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI ZAIA, JOSEPH · 2010 to 2013
$1.6M
Quantitative profiling of glycosaminoglycans from breast tumor tissue arraysR21CA177479 · NCI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI ZAIA, JOSEPH · 2014 to 2016
$712k
NCI NIH HHS R21 CA177479NCRR NIH HHS P41 RR010888NHLBI NIH HHS R01 HL098950NIGMS NIH HHS P41 GM104603
6 · The paper itself

Abstract

Glycoproteomics involves the study of glycosylation events on protein sequences ranging from purified proteins to whole proteome scales. Understanding these complex post-translational modification (PTM) events requires elucidation of the glycan moieties (monosaccharide sequences and glycosidic linkages between residues), protein sequences, as well as site-specific attachment of glycan moieties onto protein sequences, in a spatial and temporal manner in a variety of biological contexts. Compared with proteomics, bioinformatics for glycoproteomics is immature and many researchers still rely on tedious manual interpretation of glycoproteomics data. As sample preparation protocols and analysis techniques have matured, the number of publications on glycoproteomics and bioinformatics has increased substantially; however, the lack of consensus on tool development and code reuse limits the dissemination of bioinformatics tools because it requires significant effort to migrate a computational tool tailored for one method design to alternative methods. This review discusses algorithms and methods in glycoproteomics, and refers to the general proteomics field for potential solutions. It also introduces general strategies for tool integration and pipeline construction in order to better serve the glycoproteomics community. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:475-498, 2017.

Indexed as

AlgorithmsProtein Processing, Post-TranslationalCarbohydrate SequenceComputational BiologyGlycomicsGlycoproteinsGlycosidesGlycosylationHumansMass SpectrometryMonosaccharidesPeptide FragmentsPeptide MappingProteomeSoftwareGlycoproteinsGlycosidesMonosaccharidesPeptide FragmentsProteomebioinformaticsglycoinformaticsglycoproteinsglycoproteomicsmass spectrometry

Identifiers

PMID26728195
PMCPMC4931994

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.