Leveraging Foundation Models for the Characterisation 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 minimising off-target effects remains a significant challenge. Endogenous small RNAs, such as microRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs), exhibit structural features supporting their functions and are biocompatible. 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 RNA-FM model alongside interpretable biological features to systematically compare endogenous small RNAs (miRNAs and piRNAs) with synthetic siRNAs. Biological features highlighted class-specific patterns: piRNAs showed higher GC content and melting temperature than miRNAs and siRNAs, suggesting higher stability. Synthetic siRNAs bias towards adenine is consistent with design rules aimed to reduce secondary structure formation. 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 RNA exploration, we implemented these functionalities in RNAExplorer (www.rnaexplorer.com), a web-based application that allows analysing and visualising small RNA features interactively. Together, our integrative analysis provides a framework for understanding small RNA biology and improving siRNA therapeutic design strategies.