sRNARFTarget: A fast machine-learning-based approach for transcriptome-wide sRNA Target Prediction
Bibliographic record
Abstract
Abstract Bacterial small regulatory RNAs (sRNAs) are key regulators of gene expression in many processes related to adaptive responses. A multitude of sRNAs have been identified in many bacterial species; however, their function has yet to be elucidated. A key step to understand sRNAs function is to identify the mRNAs these sRNAs bind to. There are several computational methods for sRNA target prediction, and the most accurate one is CopraRNA which is based on comparative-genomics. However, species-specific sRNAs are quite common and CopraRNA cannot be used for these sRNAs. The most commonly used transcriptome-wide sRNA target prediction method and second-most-accurate method is IntaRNA. However, IntaRNA can take hours to run on a bacterial transcriptome. Here we present sRNARFTarget, a machine-learning-based method for transcriptome-wide sRNA target prediction applicable to any sRNA. We comparatively assessed the performance of sRNARFTarget, CopraRNA and IntaRNA in three bacterial species. Our results show that sRNARFTarget outperforms IntaRNA in terms of accuracy, ranking of true interacting pairs, and running time. However, CopraRNA substantially outperforms the other two programs in terms of accuracy. Thus, we suggest using CopraRNA when homolog sequences of the sRNA are available, and sRNARFTarget for transcriptome-wide prediction or for species-specific sRNAs. sRNARFTarget is available at https://github.com/BioinformaticsLabAtMUN/sRNARFTarget .
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".