Affinity purification of T7 RNA transcripts with homogeneous ends using ARiBo and CRISPR tags
Bibliographic record
Abstract
Affinity purification of RNA using the ARiBo tag technology currently provides an ideal approach to quickly prepare RNA with 3' homogeneity. Here, we explored strategies to also ensure 5' homogeneity of affinity-purified RNAs. First, we systematically investigated the effect of starting nucleotides on the 5' heterogeneity of a small SLI RNA substrate from the Neurospora VS ribozyme purified from an SLI-ARiBo precursor. A series of 32 SLI RNA sequences with variations in the +1 to +3 region was produced from two T7 promoters (class III consensus and class II 2.5) using either the wild-type T7 RNA polymerase or the P266L mutant. Although the P266L mutant helps decrease the levels of 5'-sequence heterogeneity in several cases, significant levels of 5' heterogeneity (≥1.5%) remain for transcripts starting with GGG, GAG, GCG, GGC, AGG, AGA, AAA, ACA, AUA, AAC, ACC, AUC, and AAU. To provide a more general approach to purifying RNA with 5' homogeneity, we tested the suitability of using a small CRISPR RNA stem-loop at the 5' end of the SLI-ARiBo RNA. Interestingly, we found that complete cleavage of the 5'-CRISPR tag with the Cse3 endoribonuclease can be achieved quickly from CRISPR-SLI-ARiBo transcripts. With this procedure, it is possible to generate SLI-ARiBo RNAs starting with any of the four standard nucleotides (G, C, A, or U) involved in either a single- or a double-stranded structure. Moreover, the 5'-CRISPR-based strategy can be combined with affinity purification using the 3'-ARiBo tag for quick purification of RNA with both 5' and 3' homogeneity.
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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".