Application of nucleoside or nucleotide analogues in <scp>RNA</scp> dynamics and <scp>RNA</scp>‐binding protein analysis
Bibliographic record
Abstract
Cellular RNAs undergo dynamic changes during RNA biological processes, which are tightly orchestrated by RNA-binding proteins (RBPs). Yet, the investigation of RNA dynamics is hurdled by highly abundant steady-state RNAs, which make the signals of dynamic RNAs less detectable. Notably, the exert of nucleoside or nucleotide analogue-based RNA technologies has provided a remarkable platform for RNA dynamics research, revealing diverse unnoticed features in RNA metabolism. In this review, we focus on the application of two types of analogue-based RNA sequencing, antigen-/antibody- and click chemistry-based methodologies, and summarize the RNA dynamics features revealed. Moreover, we discuss emerging single-cell newly transcribed RNA sequencing methodologies based on nucleoside analogue labeling, which provides novel insights into RNA dynamics regulation at single-cell resolution. On the other hand, we also emphasize the identification of RBPs that interact with polyA, non-polyA RNAs, or newly transcribed RNAs and also their associated RNA-binding domains at genomewide level through ultraviolet crosslinking and mass spectrometry in different contexts. We anticipated that further modification and development of these analogue-based RNA and RBP capture technologies will aid in obtaining an unprecedented understanding of RNA biology. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry RNA Methods > RNA Analyses in Cells.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".