Preparation of Mammalian Nascent RNA for Long Read Sequencing
Bibliographic record
Abstract
Abstract Long read sequencing technologies now allow high‐quality sequencing of RNAs (or their cDNAs) that are hundreds to thousands of nucleotides long. Long read sequences of nascent RNA provide single‐nucleotide‐resolution information about co‐transcriptional RNA processing events—e.g., splicing, folding, and base modifications. Here, we describe how to isolate nascent RNA from mammalian cells through subcellular fractionation of chromatin‐associated RNA, as well as how to deplete poly(A) + RNA and rRNA, and, finally, how to generate a full‐length cDNA library for use on long read sequencing platforms. This approach allows for an understanding of coordinated splicing status across multi‐intron transcripts by revealing patterns of splicing or other RNA processing events that cannot be gained from traditional short read RNA sequencing. © 2020 Wiley Periodicals LLC. Basic Protocol 1 : Subcellular fractionation Basic Protocol 2 : Nascent RNA isolation and adapter ligation Basic Protocol 3 : cDNA amplicon preparation
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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".