BacTermFinder: a comprehensive and general bacterial terminator finder using a CNN ensemble
Bibliographic record
Abstract
A terminator is a DNA region that ends the transcription process. Currently, multiple computational tools are available for predicting bacterial terminators. However, these methods are specialized for certain bacteria or terminator type (i.e. intrinsic or factor-dependent). In this work, we developed BacTermFinder using an ensemble of convolutional neural networks (CNNs) receiving as input four different representations of terminator sequences. To develop BacTermFinder, we collected roughly 41 000 bacterial terminators (intrinsic and factor-dependent) of 22 species with varying GC-content (from 28% to 71%) from published studies that used RNA-seq technologies. We evaluated BacTermFinder's performance on terminators of five bacterial species (not used for training BacTermFinder) and two archaeal species. BacTermFinder's performance was compared with that of four other bacterial terminator prediction tools. Based on our results, BacTermFinder outperforms all other four approaches in terms of average recall without increasing the number of false positives. Moreover, BacTermFinder identifies both types of terminators (intrinsic and factor-dependent) and generalizes to archaeal terminators. Additionally, we visualized the saliency map of the CNNs to gain insights on terminator motif per species. BacTermFinder is publicly available at https://github.com/BioinformaticsLabAtMUN/BacTermFinder.
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.
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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".