Evaluating Web Servers for RNA-RNA Interaction

RNA-RNA interactions (RRIs) play a critical role in post-transcriptional regulation and functional dynamics of non-coding RNAs, influencing biological processes such as splicing, translation, and gene silencing. With the expansion of non-coding RNA discovery and interaction databases, there is an increasing need for computational tools that can accurately predict RRIs. Web servers serve as accessible platforms that integrate various predictive models and user interfaces for this purpose. This review presents a comprehensive survey of existing web servers designed for RRI prediction, focusing on the computational backbone of each tool. We categorize the reviewed tools based on their machine learning (ML) or deep learning (DL) frameworks, feature extraction pipelines, and sequence- or structure-based encoding strategies. We also compare thermodynamics-based methods with hybrid approaches that integrate biological knowledge with data-driven models. Special attention is given to how each server handles RNA secondary structures, including pseudoknots, and incorporates interaction features such as energy profiles, accessibility metrics, and evolutionary conservation. Furthermore, we evaluate the usability, availability, input formats, output interpretation, and dataset support for each web server. Tools are benchmarked using publicly available RRI datasets, with a discussion on their generalizability and limitations. By highlighting trends in feature engineering, encoding schemes, and algorithmic design, this review serves as a resource for researchers aiming to select or design RRI prediction tools suited to their biological question.