Direct RNA sequencing coupled with adaptive sampling enriches RNAs of interest in the transcriptome
Bibliographic record
Abstract
Abstract Abundant cellular transcripts occupy most of the sequencing reads in the transcriptome, making it challenging to assay for low abundant transcripts. Here, we utilize the adaptive sampling function of Oxford Nanopore sequencing to selectively deplete and enrich RNAs of interest without biochemical manipulation before sequencing. Adaptive sampling performed on a pool of in vitro transcribed RNAs resulted in a 3.2-4.6-fold increase in transcripts of interest in the accept-read pool. Enriching and depleting different proportions of the candida albicans transcriptome also resulted in a 3-fold enrichment in the accepted-read pool and an 10% increase in the number of reads, with longer transcripts being more efficiently depleted. Depleting all currently annotated candida albicans transcripts revealed 26 new transcripts and isoforms, 17 of which are antisense to existing transcripts. Further improvements in the adaptive sampling of RNAs will allow the technology to be widely applied to study RNA(s) of interest in diverse transcriptomes.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| 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 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".