Identification of sRNA interacting with a transcript of interest using MS2-affinity purification coupled with RNA sequencing (MAPS) technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
RNA sequencing (RNAseq) technology recently allowed the identification of thousands of small RNAs (sRNAs) within the prokaryotic kingdom. However, drawing the comprehensive interaction map of a sRNA remains a challenging task. To address this problem, we recently developed a method called MAPS (MS2 affinity purification coupled with RNA sequencing) to characterize the full targetome of specific sRNAs. This method enabled the identification of target RNAs interacting with sRNAs, regardless of the type of regulation (positive or negative), type of targets (mRNA, tRNA, sRNA) or their abundance. We also demonstrated that we can use this technology to perform a reverse MAPS experiment, where an RNA fragment of interest is used as bait to identify interacting sRNAs. Here, we demonstrated that RybB and MicF sRNAs co-purified with internal transcribed spacers (ITS) of metZ-metW-metV tRNA transcript, confirming results obtained with MS2-RybB MAPS. Both raw and analyzed RNAseq data are available in GEO database (GSE66517).
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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 | 0.001 | 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 it