Identification of sRNA interacting with a transcript of interest using MS2-affinity purification coupled with RNA sequencing (MAPS) technology
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".