Detecting operons from RNA-seq data \nusing a convolutional and recurrent neural \nnetwork architecture
Bibliographic record
Abstract
Operon is a characteristic of prokaryotic genomes that enables the co-regulation \nof adjacent genes. Identifying which genes belong to the same operon can help in understanding \nbacterial gene function and regulation, which can enhance, for instance, \ndrug development and antibiotic resistance inhibition. There are numerous experimental \nand computational approaches for operon detection; however, many of the \ncomputational approaches have been developed for a specific target genome or require \nspecific information only available for a restricted number of bacterial genomes. \nHere, we develop a novel general method that directly utilizes RNA-seq reads as a \nsignal over nucleotide bases in the genome, extracting all the information from the \nRNA-seq data. This representation enabled us to employ deep learning techniques \nwithout limitations on species. The final model (OpDetect) demonstrates superior \nperformance in terms of recall, f1-score and Area Under Receiver Operating Characteristic \ncurve (AUROC) compared to previous approaches. Additionally, it showcases \nspecies-agnostic capabilities, successfully detecting operons even in Caenorhabditis \nelegans (C. elegans), the only eukaryotic organism known to have operons.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".