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.