High-throughput design of bacterial anti-sense RNAs using CAREng
Bibliographic record
Abstract
Summary: Short RNA (sRNA) modulation of gene expression is an increasingly popular tool for bacterial functional genomics. Antisense pairing between an sRNA and a target messenger RNA results in post-transcriptional down-regulation of a specific gene and can thus be used both for investigating individual gene function and for large-scale genetic screens. sRNAs have several advantages over knockout libraries in studies of gene function, including inducibility, the capacity to interrogate essential genes and easy portability to multiple genetic backgrounds. High-throughput, systematic design of antisense RNAs will increase the efficiency and repeatability of sRNA screens. To this end, we present CAREng, the Computer-Automated sRNA Engineer. CAREng designs antisense RNAs for all coding sequences in a given genome, while checking for potential off-targets. Availability and implementation: CAREng is available as a Python script and through a web portal (https://caren.carleton.ca). Supplementary information: online.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".