ONT DirectRNA Library preparation for poly(A) estimation v1
Bibliographic record
Abstract
This protocol provides a detailed explanation of of the steps necessary for successful Direct RNA Library preparation for Oxford Nanopore Sequencing. The protocol explains the steps needed for RNA sample preparation based on TRIzol extraction and Poly(A)Purist Mag kit enrichment prior to Direct RNA library preparation protocol. The library preparation protocol is based on the Library preparation protocols for RNA-002 kits, yet offers additional advice on what we think is important for a successful library with minimal RNA degradation. The protocol is used to assess poly(A) tail length using the tailfindr package. The poly(A) tail is a homopolymeric stretch of adenosine at the 3`-end of mature RNA transcripts and its length plays an important role in nuclear export, stability, and translational regulation of mRNA. With the introduction of native RNA sequencing by Oxford Nanopore Technologies (ONT), it is now possible to sequence full-length native RNA. A single long read contains both the transcript and the associated poly(A) tail, thereby making genome-wide transcript-specific poly(A) tail length assessment in native RNA feasible. For more information on tailfindr visit the publication or the GitHub repository
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.079 | 0.095 |
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".