Abstract
Accurate prediction of RNA secondary structures is essential for understanding the evolutionary conservation and functional roles of long noncoding RNAs (lncRNAs) across diverse species. In this study, we benchmarked two leading tools for predicting evolutionarily conserved RNA secondary structures (ECSs), SISSIz and R-scape, using two distinct experimental frameworks: one focusing on well-characterized mitochondrial RNA structures and the other on experimentally validated Rfam structures embedded within simulated genome alignments. While both tools performed similarly overall, each displayed subtle preferences in detecting ECSs. To overcome their individual limitations, we evaluated two interpretable machine learning approaches that integrate the strengths of both methods. By balancing thermodynamic stability features from RNALalifold and SISSIz with robust covariation metrics from R-scape, a random forest model classifier significantly outperformed individual tools in identifying ECSs. This classifier was implemented in ECSFinder, a new tool designed for large-scale comparative genomics applications, that can provide robust, genome-wide identification of conserved RNA structures, and offer valuable insights into the modular elements and evolutionary conservation of lncRNAs.