Abstract Background Bacterial transcription termination is a critical yet underexplored layer of gene regulation in microbial ecosystems. Existing computational tools, however, primarily focus on predicting transcript 3′ ends generated by Rho-independent terminators (RITs) in a few model species, leaving gaps in understanding those generated by Rho-dependent terminators (RDTs) and their diversity across Bacteria. Results We developed BATTER (Bacteria Transcript Three Prime End Recognizer), a deep learning-based framework for predicting bacterial transcript 3′ termini. BATTER leverages the observation that conserved stem-loop structures are frequently associated with 3′ ends of primary transcripts terminated by both RIT and RDT mechanisms across diverse bacterial clades. Compared with existing approaches, BATTER demonstrated superior performance and scalability, enabling a comprehensive analysis of 42,905 representative bacterial genomes. This large-scale application revealed that stem-loop structures exhibit clade-specific properties with greater variations between species than between gene families. Notably, BATTER uncovered that certain Cyanobacteria lineages, despite lacking rho homologs, harbor Rho utilization (RUT)-like sequences near 3′ ends, and preliminary experimental validation in E. coli supports their partial functionality in transcription termination. Additionally, BATTER systematically identified pervasive premature termination events in antimicrobial resistance (AMR) genes. Conclusions BATTER enables large-scale comparative genomic analyses of transcription termination, providing a powerful framework to investigate termination-associated transcriptional regulation in microbial communities. The BATTER tool is available at https://github.com/xu-research-lab/BATTER .
Jin et al. (Mon,) studied this question.