PhOxi-seq Detects Enzyme-Dependent m <sup>2</sup> G in Multiple RNA Types
Bibliographic record
Abstract
In recent years, RNA-modifying enzymes have gained significant attention due to their impact on critical RNA-based processes and, consequently, human pathology. However, identifying sites of modifications throughout the transcriptome remains challenging largely due to the lack of accurate and sensitive detection technologies. Recently, we described PhOxi-seq as a method capable of confirming known sites of m 2 G within abundant classes of RNA, namely, purified rRNA and purified tRNA. Here, we further explore the selectivity of PhOxi-seq and describe an optimized PhOxi-seq workflow, coupled to a novel bioinformatic pipeline, that is capable of detecting enzyme-dependent m 2 G sites throughout the transcriptome. In this way, we generated a database of potential THUMPD3-dependent m 2 G sites in multiple RNA classes within a human cancer cell line and further identify potential non-THUMPD3 controlled m 2 G sites. These potential sites should serve as the basis for further confirmation studies for m 2 G within the human transcriptome.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".