An overview of tools to decipher O-GlcNAcylation from historical approaches to new insights
Bibliographic record
Abstract
O-GlcNAcylation is a post-translational modification which affects approximately 5000 human proteins. Its involvement has been shown in many if not all biological processes. Variations in O-GlcNAcylation levels can be associated with the development of diseases. Deciphering the role of O-GlcNAcylation is an important issue to (i) understand its involvement in pathophysiological development and (ii) develop new therapeutic strategies to modulate O-GlcNAc levels. Over the past 30 years, despite the development of several approaches, knowledge of its role and regulation have remained limited. This review proposes an overview of the currently available tools to study O-GlcNAcylation and identify O-GlcNAcylated proteins. Briefly, we discuss pharmacological modulators, methods to study O-GlcNAcylation levels and approaches for O-GlcNAcylomic profiling. This review aims to contribute to a better understanding of the methods used to study O-GlcNAcylation and to promote efforts in the development of new strategies to explore this promising modification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".