Leveraging Foundation Models for the Characterization of Small-RNA Properties

Small interfering RNAs (siRNAs) provide a promising therapeutic approach capable of selectively silencing disease-associated genes; however, achieving high efficacy and specificity while minimizing off-target effects remains a marked challenge. Endogenous small RNAs, such as microRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs), exhibit structural features supporting their functions and interactions with other biomolecules. Recent advances in RNA foundation models, such as RNA-FM, enable large-scale learning of sequence and structural representations of RNA sequences, offering a powerful framework for studying small-RNA functions. Here, we leverage the RNA-FM alongside interpretable biological features to systematically compare endogenous small RNAs (miRNAs and piRNAs) with synthetic siRNAs. Biological features highlighted type-specific patterns: piRNAs showed significantly higher GC content and melting temperature than miRNAs and siRNAs, suggesting higher stability. Importantly, we mapped RNA-FM embeddings to interpretable features to better understand deep-learning outputs and facilitate effective extraction of functionally relevant information. To support predictive and comparative analyses of small RNAs, we implemented these functionalities in RNAExplorer (www.rnaexplorer.com), a web-based application that allows analyzing and visualizing small-RNA features interactively. Together, our integrative analysis provides a framework for understanding small-RNA biology and improving siRNA therapeutic design strategies.