Application of nucleoside or nucleotide analogues in <scp>RNA</scp> dynamics and <scp>RNA</scp>‐binding protein analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it