Bacterial small RNAs (sRNAs) are pivotal in post-transcriptional regulation, affecting functions like virulence, metabolism, and gene expression by binding specific mRNA targets. Identifying these targets is crucial to understanding sRNA regulation across species. Despite advancements in high-throughput (HT) experimental methods, they remain technically challenging and are limited to detecting sRNA-target interactions under specific environmental conditions. Therefore, computational approaches, especially machine learning (ML), are essential for identifying strong candidates for biological validation. In this paper, we hypothesize that ML models trained on large-scale interaction data from specific conditions can accurately predict new interactions in unseen conditions within the same bacterial strain. To test this, we developed models from two families: (1) graph neural networks (GNNs), including GraphRNA and kGraphRNA, that learn transformed representations of interacting sRNA-mRNA pairs via graph relationships, and (2) decision forests, sInterRF (Random Forest) and sInterXGB (XGBoost), which use various interaction features for prediction. We also proposed Summation Ensemble Models (SEM) that combine scores from multiple models. Across three seen-to-unseen conditions evaluations, our models -particularly kGraphRNA- significantly improved the area under the ROC curve (AUC) and Precision-Recall curve (PR-AUC) compared to sRNARFTarget, CopraRNA, and RNAup. The SEM model combining GraphRNA and CopraRNA outperformed CopraRNA alone on a low-throughput (LT) interactions test set (HT-to-LT evaluation). Beyond enhanced performance, our models enable target prediction for species-specific sRNAs, a capability lacking in some existing tools. Furthermore, GNN models remove the dependency on external tools like RNAplex or RNAup to compute hybridization duplex or energy features, enhancing scalability and runtime efficiency. While this study focuses on E. coli K12 MG1655 interactions, our methods are fully adaptable to predict interactions in other bacterial strains, given sufficient data for training. Our comprehensive feature importance analysis revealed the complexity of sRNA-mRNA interactions across environmental conditions, underscoring the significance of RNA sequence composition and duplex structure characteristics, like base pairing and energy factors; findings that align with biological evidence from previous studies. As HT experiments expand sRNA-target interaction data across conditions in various bacteria, our ML methods with features analysis offer promising advances in sRNA-target prediction and deeper insights into sRNA regulatory mechanisms across diverse species.

SUMMARY: sInterBase is a comprehensive and easy-to-operate web-based platform for mining experimentally identified sRNA-mRNA interactions in Escherichia coli. Interactions in the database are annotated with an interaction duplex and a set of descriptive features. sInterBase provides advanced functionality, such as flexible search based on various criteria, statistical analysis via charts, browsing, and downloading interactions for further use. AVAILABILITY AND IMPLEMENTATION: sInterBase is available at https://sinterbase.cs.bgu.ac.il/.

MOTIVATION: High-resolution microbial strain typing is essential for various clinical purposes, including disease outbreak investigation, tracking of microbial transmission events and epidemiological surveillance of bacterial infections. The widely used approach for multilocus sequence typing (MLST) that is based on the core genome, cgMLST, has the advantage of a high level of typeability and maximal discriminatory power. Yet, the transition from a seven loci-based scheme to cgMLST involves several challenges, that include the need by some users to maintain backward compatibility, growing difficulties in the day-to-day communication within the microbiology community with respect to nomenclature and ontology, issues with typeability, especially if a more stringent approach to loci presence is used, and computational requirements concerning laboratory data management and sharing with end-users. Hence, methods for optimizing cgMLST schemes through careful reduction of the number of loci are expected to be beneficial for practical needs in different settings. RESULTS: We present a new machine learning-based methodology, minMLST, for minimizing the number of genes in cgMLST schemes by identifying subsets of informative genes and analyzing the trade-off between gene reduction and typing performance. The results achieved with minMLST over eight bacterial species show that despite the reduction in the number of genes up to a factor of 10, the typing performance remains very high and significant with an Adjusted Rand Index that ranges between 0.4 and 0.93 in different species and a P-value < 10-3. The identification of such optimized MLST schemes for bacterial strain typing is expected to improve the implementation of cgMLST by improving interlaboratory agreement and communication. AVAILABILITY AND IMPLEMENTATION: The python package minMLST is available at https://PyPi.org/project/minmlst/PyPI and supported on Linux and Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.