DDTRN: predicting bacterial transcriptional regulatory networks based on gene sequences using dual descriptor.

Abstract

Accurate computational reconstruction of bacterial transcriptional regulatory network (TRN) remains a fundamental challenge in systems biology, particularly for non-model organisms lacking extensive transcriptomic data. We present DDTRN, a sequence-driven framework that formulates TRN inference as a binary classification task over concatenated regulator-target gene sequence pairs and employs a Dual Descriptor (DD) model to predict regulatory interactions. The DD architecture represents a sequence using two learnable components: Composition Weight Map (CWM) and Position Weight Function (PWF). We comprehensively evaluate DDTRN against six conventional machine learning (ML) baselines across eight benchmark bacterial datasets, including Escherichia coli (DREAM5, RegulonDB), Bacillus subtilis, Salmonella enterica, Corynebacterium glutamicum, Mycobacterium tuberculosis, Pseudomonas aeruginosa, and Streptomyces coelicolor. DDTRN achieves superior overall performance, attaining average area under the receiver operating characteristic curve and area under the precision-recall curve scores of 0.869 and 0.868, respectively, with particularly pronounced advantages at lower descriptor ranks where positional weighting compensates for limited sequence context. Systematic sensitivity analyses of rank, embedding dimension, and basis function count reveal stable optimal operating regimes, while subsampling experiments demonstrate strong robustness even with limited training data. Interpretability analyses show that PWF learns distinct periodic contributions across different rank granularities and that CWM preferentially weights meaningful k-mers. A case study on the E. coli dataset further illustrates that DDTRN identifies method-specific candidate targets complementary to those proposed by conventional approaches. By operating solely on genomic sequence, DDTRN provides a scalable, interpretable, and data-efficient framework for bacterial TRN inference in species where expression data are scarce, and it establishes a foundation for future multimodal integration with condition-specific regulatory information.