High-throughput design of bacterial anti-sense RNAs using CAREng
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it